Radiant heating controls and methods for an environmental control system

ABSTRACT

Embodiments of the invention describe thermostats that use model predictive controls and related methods. A method of controlling a thermostat using a model predictive control may involve determining a parameterized model. The parameterized model may be used to predicted ambient temperature values for an enclosure. A set of radiant heating system control strategies may be selected for evaluation to determine an optimal control strategy from the set of control strategies. To determine the optimal control strategy, a predictive algorithm may be executed, in which each control strategy is applied to the parameterized model to predict an ambient temperature trajectory and each ambient temperature trajectory is processed in view of a predetermined assessment function. Processing the ambient temperature trajectory in this manner may involve minimizing a cost value associated with the ambient temperature trajectory. The radiant heating system may subsequently be controlled according to the selected optimal control strategy.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.13/632,152 (Attorney Docket No. 94021-853384 NES0259-US), filed Sep. 30,2012, entitled “RADIANT HEATING CONTROLS AND METHODS FOR ANENVIRONMENTAL CONTROL SYSTEM,” the entire disclosure of which is herebyincorporated by reference for all purposes, as if fully set forthherein.

TECHNICAL FIELD

This patent specification relates to systems and methods for controllingheating systems to minimize overshooting and undershooting effects. Moreparticularly, this patent specification relates to control units thatgovern the operation of energy-consuming systems, household devices, orother resource-consuming systems, including systems and methods forcontrolling heating, ventilation, and air conditioning (HVAC) systems.

BACKGROUND OF THE INVENTION

Substantial effort and attention continue toward the development ofnewer and more sustainable energy supplies. The conservation of energyby increased energy efficiency remains crucial to the world's energyfuture. According to an October 2010 report from the U.S. Department ofEnergy, heating and cooling account for 56% of the energy use in atypical U.S. home, making it the largest energy expense for most homes.Along with improvements in the physical plant associated with homeheating and cooling (e.g., improved insulation, higher efficiencyfurnaces), substantial increases in energy efficiency can be achieved bybetter control and regulation of home heating and cooling equipment. Oneparticular energy inefficient operation involves what is commonly knownas “overshooting” and “undershooting” in which, for overshooting, anambient temperature continues to rise above a setpoint temperature eventhough the heating operation has been discontinued, while forundershooting, the ambient temperature continues to fall below thesetpoint temperature even after the heating operation has been resumed.This problem is particularly evident in radiant heating systems andoften results in less than ideal comfort conditions. The overshoot andundershoot are often due to a thermal inertia of a heated enclosure orthe amount of mass that is heated. Conventional control systems arelimited in their effectiveness in avoiding undesirable overshooting andundershooting effects.

As discussed in the technical publication No. 50-8433, entitled “PowerStealing Thermostats” from Honeywell (1997), early thermostats used abimetallic strip to sense temperature and respond to temperature changesin the room. The movement of the bimetallic strip was used to directlyopen and close an electrical circuit. Power was delivered to anelectromechanical actuator, usually relay or contactor in the HVACequipment whenever the contact was closed to provide heating and/orcooling to the controlled space. Since these thermostats did not requireelectrical power to operate, the wiring connections were very simple.Only one wire connected to the transformer and another wire connected tothe load. Typically, a 24 VAC power supply transformer, the thermostat,and 24 VAC HVAC equipment relay were all connected in a loop with eachdevice having only two required external connections.

When electronics began to be used in thermostats, the fact that thethermostat was not directly wired to both sides of the transformer forits power source created a problem. This meant that the thermostat hadto be hardwired directly from the system transformer. Direct hardwiringa common “C” wire from the transformer to the electronic thermostat maybe very difficult and costly.

Because many households do not have a direct wire from the systemtransformer (such as a “C” wire), some thermostats have been designed toderive power from the transformer through the equipment load. Themethods for powering an electronic thermostat from the transformer witha single direct wire connection to the transformer are called “powerstealing” or “power sharing” methods. The thermostat “steals,” “shares,”or “harvests” its power during the “OFF” periods of the heating orcooling system by allowing a small amount of current to flow through itinto the load coil below the load coil's response threshold (even atmaximum transformer output voltage). During the “ON” periods of theheating or cooling system the thermostat draws power by allowing a smallvoltage drop across itself. Ideally, the voltage drop will not cause theload coil to dropout below its response threshold (even at minimumtransformer output voltage). Examples of thermostats with power stealingcapability include the Honeywell T8600, Honeywell T8400C, and theEmerson Model 1F97-0671. However, these systems do not have powerstorage means and therefore must always rely on power stealing.

Additionally, microprocessor controlled “intelligent” thermostats mayhave more advanced environmental control capabilities that can saveenergy while also keeping occupants comfortable. To do this, thesethermostats require more information from the occupants as well as theenvironments where the thermostats are located. These thermostats mayalso be capable of connection to computer networks, including both localarea networks (or other “private” networks) and wide area networks suchas the Internet (or other “public” networks), in order to obtain currentand forecasted outside weather data, cooperate in so-calleddemand-response programs (e.g., automatic conformance with power alertsthat may be issued by utility companies during periods of extremeweather), enable users to have remote access and/or control thereofthrough their network-connected device (e.g., smartphone, tabletcomputer, PC-based web browser), and other advanced functionalities thatmay require network connectivity.

Issues arise in relation to providing microprocessor-controlledthermostats using high-powered user interfaces, one or more such issuesbeing at least partially resolved by one or more of the embodimentsdescribed herein below. On the one hand, it is desirable to provide athermostat having advanced functionalities such as those associated withrelatively powerful microprocessors and reliable wireless communicationschips. On the other hand, it is desirable to provide a thermostat thatis compatible and adaptable for installation in a wide variety of homes,including a substantial percentage of homes that are not equipped withthe “C” wire discussed above. It is still further desirable to providesuch a thermostat that accommodates easy do-it-yourself installationsuch that the expense and inconvenience of arranging for an HVACtechnician to visit the premises to install the thermostat can beavoided for a large number of users. It is still further desirable toprovide a thermostat having such processing power, wirelesscommunications capabilities, visually pleasing display qualities, andother advanced functionalities, while also being a thermostat that, inaddition to not requiring a “C” wire, likewise does not need to beplugged into a household line current or a so-called “power brick,”which can be inconvenient for the particular location of the thermostatas well as unsightly. Therefore, improvements are needed in the art.

Important issues arise, moreover, at the interface between (i)energy-saving technologies that might be achievable using known sensingand processing methods, and (ii) the actual widespread user adoption ofdevices that implement such energy-saving technologies and theintegration of those devices into their daily routines and environment.It has been found especially important that the contact between a userand an energy-saving device, which for the case of a thermostat wouldinclude both (i) the quality and enjoyability of the user experiencewhen interfacing with the thermostat, as well as (ii) the physicalcomfort provided by the way in which the ambient temperature iscontrolled by the thermostat, constitute a particularly pleasantexperience, or else the user can quickly “turn off” or “tune out” to thedevice and its energy-saving advantages, such as by de-activating theadvanced features (for example, setting their thermostat to a“temporary” manual-override mode on a permanent basis) or even taking itback to the seller and replacing it with their old device or a “lesscomplicated” device. One or more issues arises in the context ofproviding an intelligent, multi-sensing, network-connected,energy-saving device, including a device that intelligently controlsradiant heating systems, that provides a pleasant user overall userexperience including effective and appropriate control of ambienttemperature. Other issues arise as would be apparent to a person skilledin the art in view of the present teachings.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the invention describe devices, systems, and method usingpredictive controls to condition an enclosure, such as a home. Suchcontrols may enhance the functionality of HVAC systems, especially whenused with radiant heating systems. According to one aspect, a thermostatis described herein. The thermostat includes a housing, a memory, and aprocessing system disposed within the housing. The processing system maybe in operative communication with one or more temperature sensors todetermine an ambient temperature in an enclosure and may be in operativecommunication with the memory. The processing system may also be inoperative communication with a radiant heating system to heat theenclosure via radiant heating so that the ambient temperature is near asetpoint temperature.

In one embodiment, the processing system may determine a parameterizedmodel from which a predicted value for the ambient temperature of theenclosure responsive to a candidate radiant heating control strategy isdetermined. The parameterized model may be based on historical ambienttemperatures for the enclosure that are acquired by the thermostatduring associated historical periods in which radiant heat control wasactuated by the thermostat and stored in the memory. The processingsystem may also select a set of candidate control strategies for use incontrolling the radiant heating system. Each candidate control strategymay be a binary-valued control trajectory having a candidate overallon-time percentage over a predefined candidate control duration. Eachcandidate control strategy may also be constrained to have a minimumnumber of on-time cycles that achieves the candidate overall on-timepercentage.

The processing system may further execute a predictive algorithm todetermine an optimal control strategy from the set of candidate controlstrategies. According to some embodiments, this determination may bemade by applying each candidate control strategy to the parameterizedmodel to predict a corresponding ambient temperature trajectory andprocessing each corresponding ambient temperature trajectory in view ofone or more predetermined assessment functions to select an optimal oneof the candidate control strategies according to one or morepredetermined assessment criteria. The one or more predeterminedassessment functions may include a cost function in which a cost isincreased as an ambient temperature trajectory of a respective candidatecontrol strategy deviates from the setpoint temperature. The processingsystem may further control the radiant heating system according to theselected optimal control strategy.

In some embodiments, each candidate control strategy may exhibit asingle on-time to off-time cycle transition over the predefinedcandidate control duration. In other embodiments, the radiant heatingsystem may not be able to perform an on-time to off-time cycletransition more than twice during the predefined candidate controlduration. According to one embodiment, the on-time cycles and off-timecycles may have intervals of not less than 10 minutes. The processingsystem may additionally determine a Lag value that represents an amountof thermal mass or inertia for the enclosure. The parameterized modelmay include predetermined response trajectories, wherein weightingcoefficients are found or calculated for the predetermined responsetrajectories.

In some embodiments, the parameterized model is based on a combinationof historical solar radiation and a radiant heating response dataacquired during associated historical periods. In such embodiments,applying each candidate control strategy to the parameterized model mayinclude using a solar radiation function and a radiant heating responsefunction to predict the corresponding ambient temperature trajectory.The parameterized model may be further based on historical outsidetemperature data acquired during associated historical periods. In theseembodiments, applying each candidate control strategy to theparameterized model may include using forecasted temperature data topredict the corresponding ambient temperature trajectory.

In other embodiments, the parameterized model may be further based onhistorical data acquired during associated historical periods for one ormore of the following data types: seasonal climate change data, humiditydata, rainfall data, snowpack data, and/or elevation data. In suchembodiments, applying each candidate control strategy to theparameterized model may include using forecasted data or otherwiseselected data for the one or more data types to predict thecorresponding ambient temperature trajectory.

According to some embodiments, the processing system may limit a cycletransition of the radiant heating system (i.e., either on or off) whilethe ambient temperature is outside of a defined maintenance band of thesetpoint temperature. In another embodiment, the processing system mayincrease an offset value of a maintenance band that defines an upperthreshold temperature and a lower threshold temperature relative to thesetpoint temperature. The offset value may be increased based on anincreased confidence that the parameterized model characterizes thehistorical ambient temperatures.

According to another aspect, a method of controlling a thermostat usingmodel predictive control is described herein. According to the method, athermostat having a housing, a memory, and a processing system disposedwithin the housing is provided. As described herein, the processingsystem may be in operative communication with one or more temperaturesensors to determine an ambient temperature in an enclosure and may bein operative communication with the memory. The processing system mayalso be in operative communication with a radiant heating system to heatthe enclosure via radiant heating so that the ambient temperature isnear a setpoint temperature. According to the method, a parameterizedmodel may be determined from which a predicted value for the ambienttemperature of the enclosure responsive to a candidate radiant heatingcontrol strategy is determined. The parameterized model may be based onhistorical ambient temperatures for the enclosure acquired by thethermostat during associated historical periods in which radiant heatcontrol was actuated by the thermostat and stored in the memory.

According to the method, a set of candidate control strategies for usein controlling the radiant heating system may be selected. Eachcandidate control strategy may be a binary-valued control trajectoryhaving a candidate overall on-time percentage over a predefinedcandidate control duration. In addition, each candidate control strategymay be constrained to have a minimum number of on-time cycles thatachieves the candidate overall on-time percentage. According to themethod, a predictive algorithm may be executed to determine an optimalcontrol strategy from the set of candidate control strategies. Thisdetermination may be performed by applying each candidate controlstrategy to the parameterized model to predict a corresponding ambienttemperature trajectory and processing each corresponding ambienttemperature trajectory in view of one or more predetermined assessmentfunctions to select an optimal one of the candidate control strategiesaccording to one or more predetermined assessment criteria. As describedherein, the one or more predetermined assessment functions may include acost function in which a cost is increased as an ambient temperaturetrajectory of a respective candidate control strategy deviates from thesetpoint temperature. According to the method, the radiant heatingsystem may be controlled according to the selected optimal controlstrategy.

In some embodiments, it may be determined as to whether the modelpredictive control provides enhanced control of the radiant heatingsystem relative to an additional control method prior to using the modelpredictive control. A Lag value may also be calculated, measured, orotherwise determined that represents an amount of thermal mass orinertia for the enclosure. In some embodiments, the on-time cycles andoff-time cycles may have intervals of not less than 10 minutes. In someembodiments, the parameterized model may include predetermined responsetrajectories and the method may additionally include: determiningweighting coefficients of the predetermined response trajectories.

As described herein, the parameterized model may be based on acombination of historical solar radiation and a radiant heating responsedata acquired during associated historical periods. In such embodiments,applying each candidate control strategy to the parameterized model mayinclude using a solar radiation function and a radiant heating responsefunction to predict the corresponding ambient temperature trajectory. Insome embodiments, the parameterized model may be further based onhistorical outside temperature data acquired during associatedhistorical periods. In such embodiments, applying each candidate controlstrategy to the parameterized model may include using forecastedtemperature data to predict the corresponding ambient temperaturetrajectory.

According to some methods, a cycle transition of the radiant heatingsystem may be limited or restricted while the ambient temperature isoutside of a defined maintenance band of the setpoint temperature.According to another method, an offset value of a maintenance band maybe adjusted, the offset value defining an upper threshold temperatureand a lower threshold temperature relative to the setpoint temperature.The offset value may be adjusted based on a confidence that theparameterized model characterizes the historical ambient temperatures.

According to another aspect, a thermostat is described herein. Thethermostat includes a housing, a memory, and a processing systemdisposed within the housing. The processing system may be configured inoperative communication with one or more temperature sensors, thememory, and a radiant heating system for the reasons described above.According to one embodiment, the processing system may determine aparameterized model from which a predicted value for the ambienttemperature of the enclosure responsive to a candidate radiant heatingcontrol strategy is determined. The parameterized model may be based onhistorical ambient temperatures for the enclosure acquired by thethermostat during associated historical periods in which radiant heatcontrol was actuated by the thermostat and stored in the memory. Aconfidence metric may be associated with the parameterized model.

The processing system may also determine a maintenance band foroperation of the radiant heating system. The maintenance band may havean offset value that defines an upper threshold temperature and a lowerthreshold temperature relative to the setpoint temperature. Themaintenance band may be used in controlling on-cycle and off-cycletransitions of the radiant heating system. In some embodiments, theoffset value may be dependent on the confidence metric of theparameterized model. For example, the offset value may be greater if theconfidence metric is large and may be smaller if the confidence metricis small. The processing system may also execute a predictive algorithmto determine an optimal control strategy from a set of candidate controlstrategies by applying each candidate control strategy to theparameterized model to predict a corresponding ambient temperaturetrajectory. The processing system may further control the radiantheating system according to the determined optimal control strategyusing the maintenance band.

According to one embodiment, each candidate control strategy may be abinary-valued control trajectory having a candidate overall on-timepercentage over a predefined candidate control duration. In addition,each candidate control strategy may be constrained to have a minimumnumber of on-time cycles that achieves the candidate overall on-timepercentage. In some embodiment, executing the predictive algorithm mayalso include processing each corresponding ambient temperaturetrajectory in view of one or more predetermined assessment functions toselect an optimal one of the candidate control strategies according toone or more predetermined assessment criteria.

According to another aspect, a method of controlling a thermostat isdescribed herein. According to the method, a thermostat may be providedthat includes a housing, a memory, and a processing system disposedwithin the housing. The processing system may be in operativecommunication with one or more temperature sensors, with the memory, andwith a radiant heating system for the reasons described above. Themethod may include determining a first parameterized model from which apredicted value for the ambient temperature of the enclosure responsiveto a candidate radiant heating control strategy may be determined. Theparameterized model may be associated with a confidence metric. Themethod may also include determining a maintenance band for operation ofthe radiant heating system, the maintenance band having an offset valuethat defines an upper threshold temperature and a lower thresholdtemperature relative to the setpoint temperature that is used incontrolling on-cycle and off-cycle transitions of the radiant heatingsystem.

The method may further include adjusting the offset value based on theconfidence metric such that the offset value is greater if theconfidence metric is large and smaller if the confidence metric issmall. The method may additionally include executing a predictivealgorithm to determine an optimal control strategy from a set ofcandidate control strategies by applying each candidate control strategyto the parameterized model to predict a corresponding ambienttemperature trajectory. The method may additionally include controllingthe radiant heating system according to the determined optimal controlstrategy using the first maintenance band.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an enclosure with an HVAC system, according tosome embodiments.

FIG. 2 is a diagram of an HVAC system, according to some embodiments.

FIG. 3 illustrates a perspective view of a thermostat, according to oneembodiment.

FIG. 4 illustrates an exploded perspective view of a thermostat having ahead unit and the backplate, according to one embodiment.

FIG. 5A illustrates an exploded perspective view of a head unit withrespect to its primary components, according to one embodiment.

FIG. 5B illustrates an exploded perspective view of a backplate withrespect to its primary components, according to one embodiment.

FIG. 6A illustrates a simplified functional block diagram for a headunit, according to one embodiment.

FIG. 6B illustrates a simplified functional block diagram for abackplate, according to one embodiment.

FIG. 7 illustrates a simplified circuit diagram of a system for managingthe power consumed by a thermostat, according to one embodiment.

FIG. 8A illustrates a method for a time to temperature computation,according to one embodiment.

FIG. 8B illustrates a conceptual diagram of the method of FIG. 8A,according to one embodiment.

FIG. 9 illustrates a maintenance band that may be used in HVAC controls,according to one embodiment.

FIG. 10 illustrates predicted ambient temperature trajectories that maybe calculated by a predictive control algorithm, according to oneembodiment.

FIG. 11 illustrates a model of a solar radiation curve, according to oneembodiment.

FIG. 12 illustrates a triangular model of an activation function,according to one embodiment.

FIG. 13 illustrates a graph showing modeled effects of Lag-delayedheating versus a radiant heater state based on the activation functionof FIG. 12, according to one embodiment.

FIG. 14 illustrates a predictive control algorithm in control systemform, according to one embodiment.

FIG. 15 illustrates a calculation of a temperature variation in anenclosure using a prediction model equation, according to oneembodiment.

FIG. 16 illustrates a histogram of a calculated fit for approximately600 thermostat devices over a two month period, according to oneembodiment.

FIG. 17 illustrates a defined subset of control strategies that may beused when an ambient temperature measurement is below a lowermaintenance band threshold, according to one embodiment.

FIG. 18 illustrates a defined subset of control strategies that may beused when an ambient temperature measurement is above an uppermaintenance band threshold, according to one embodiment.

FIG. 19 illustrates a defined subset of control strategies that may beused when an ambient temperature measurement is within maintenance band,according to one embodiment.

FIG. 20 illustrates a method of controlling a thermostat using a modelpredictive control, according to one embodiment.

FIG. 21 illustrates a method of controlling a thermostat, according toone embodiment.

FIG. 22 illustrates steps for automated system matching, according toone embodiment.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, for purposes of explanation,numerous specific details are set forth to provide a thoroughunderstanding of the various embodiments of the present invention. Thoseof ordinary skill in the art will realize that these various embodimentsof the present invention are illustrative only and are not intended tobe limiting in any way. Other embodiments of the present invention willreadily suggest themselves to such skilled persons having the benefit ofthis disclosure.

In addition, for clarity purposes, not all of the routine features ofthe embodiments described herein are shown or described. One of ordinaryskill in the art would readily appreciate that in the development of anysuch actual embodiment, numerous embodiment-specific decisions may berequired to achieve specific design objectives. These design objectiveswill vary from one embodiment to another and from one developer toanother. Moreover, it will be appreciated that such a development effortmight be complex and time-consuming but would nevertheless be a routineengineering undertaking for those of ordinary skill in the art havingthe benefit of this disclosure.

The subject matter of the instant disclosure is related to the subjectmatter of the following commonly assigned applications, each of which isincorporated by reference herein: U.S. Prov. Ser. No. 61/550,343 filedOct. 21, 2011; U.S. Prov. Ser. No. 61/550,346 filed Oct. 21, 2011;International Application Ser. No. PCT/US12/00007 filed Jan. 3, 2012;U.S. Ser. No. 13/467,025 filed May 8, 2012; U.S. Ser. No. 13/632,093(Attorney Docket No. NES0122-US) filed even date herewith and entitled,“Intelligent Controller For An Environmental Control System”; U.S. Ser.No. 13/632,028 filed Sep. 30, 2012 and entitled, “Intelligent ControllerProviding Time to Target State”; U.S. Ser. No. 13/632,041 filed Sep. 30,2012 and entitled, “Automated Control-Schedule Acquisition Within AnIntelligent Controller”; U.S. Ser. No. 13/632,070 filed Sep. 30, 2012and entitled, “Automated Presence Detection and Presence-Related ControlWithin An Intelligent Controller”; U.S. Ser. No. 13/632,150 filed Sep.30, 2012 and entitled, “Preconditioning Controls and Methods For AnEnvironmental Control System”; and U.S. Ser. No. 13/632,148 filed Sep.30, 2012 and entitled, “HVAC Controller With User-Friendly InstallationFeatures Facilitating Both Do-It-Yourself and Professional InstallationScenarios”.

It is to be appreciated that while one or more embodiments are describedfurther herein in the context of typical HVAC system used in aresidential home, such as single-family residential home, the scope ofthe present teachings is not so limited. More generally, thermostatsaccording to one or more of the preferred embodiments are applicable fora wide variety of enclosures having one or more HVAC systems including,without limitation, duplexes, townhomes, multi-unit apartment buildings,hotels, retail stores, office buildings and industrial buildings.Further, it is to be appreciated that while the terms user, customer,installer, homeowner, occupant, guest, tenant, landlord, repair person,and the like may be used to refer to the person or persons who areinteracting with the thermostat or other device or user interface in thecontext of one or more scenarios described herein, these references areby no means to be considered as limiting the scope of the presentteachings with respect to the person or persons who are performing suchactions.

Provided according to one or more embodiments are systems, methods,computer program products, and related business methods for controllingone or more HVAC systems based on one or more versatile sensing andcontrol units (VSCU units), each VSCU unit being configured and adaptedto provide sophisticated, customized, energy-saving HVAC controlfunctionality while at the same time being visually appealing,non-intimidating, elegant to behold, and delightfully easy to use. Theterm “thermostat” is used hereinbelow to represent a particular type ofVSCU unit (Versatile Sensing and Control) that is particularlyapplicable for HVAC control in an enclosure. Although “thermostat” and“VSCU unit” may be seen as generally interchangeable for the contexts ofHVAC control of an enclosure, it is within the scope of the presentteachings for each of the embodiments hereinabove and hereinbelow to beapplied to VSCU units having control functionality over measurablecharacteristics other than temperature (e.g., pressure, flow rate,height, position, velocity, acceleration, capacity, power, loudness,brightness) for any of a variety of different control systems involvingthe governance of one or more measurable characteristics of one or morephysical systems, and/or the governance of other energy or resourceconsuming systems such as water usage systems, air usage systems,systems involving the usage of other natural resources, and systemsinvolving the usage of various other forms of energy.

FIG. 1 is a diagram illustrating an exemplary enclosure using athermostat 110 implemented in accordance with the present invention forcontrolling one or more environmental conditions. For example, enclosure100 illustrates a single-family dwelling type of enclosure using alearning thermostat 110 (also referred to for convenience as “thermostat110”) for the control of heating and cooling provided by an HVAC system120. Alternate embodiments of the present invention may be used withother types of enclosures including a duplex, an apartment within anapartment building, a light commercial structure such as an office orretail store, or a structure or enclosure that is a combination of theseand other types of enclosures.

Some embodiments of thermostat 110 in FIG. 1 incorporate one or moresensors to gather data from the environment associated with enclosure100. Sensors incorporated in thermostat 110 may detect occupancy,temperature, light and other environmental conditions and influence thecontrol and operation of HVAC system 120. Sensors incorporated withinthermostat 110 do not protrude from the surface of the thermostat 110thereby providing a sleek and elegant design that does not drawattention from the occupants in a house or other enclosure. As a result,thermostat 110 readily fits with almost any décor while adding to theoverall appeal of the interior design.

As used herein, a “learning” thermostat refers to a thermostat, or oneof plural communicating thermostats in a multi-thermostat network,having an ability to automatically establish and/or modify at least onefuture setpoint in a heating and/or cooling schedule (see FIG. 10) basedon at least one automatically sensed event and/or at least one past orcurrent user input.

As used herein, a “primary” thermostat refers to a thermostat that iselectrically connected to actuate all or part of an HVAC system, such asby virtue of electrical connection to HVAC control wires (e.g. W, G, Y,etc.) leading to the HVAC system.

As used herein, an “auxiliary” thermostat refers to a thermostat that isnot electrically connected to actuate an HVAC system, but that otherwisecontains at least one sensor and influences or facilitates primarythermostat control of an HVAC system by virtue of data communicationswith the primary thermostat.

In one particularly useful scenario, the thermostat 110 is a primarylearning thermostat and is wall-mounted and connected to all of the HVACcontrol wires, while the remote thermostat 112 is an auxiliary learningthermostat positioned on a nightstand or dresser, the auxiliary learningthermostat being similar in appearance and user-interface features asthe primary learning thermostat, the auxiliary learning thermostatfurther having similar sensing capabilities (e.g., temperature,humidity, motion, ambient light, proximity) as the primary learningthermostat, but the auxiliary learning thermostat not being connected toany of the HVAC wires. Although it is not connected to any HVAC wires,the auxiliary learning thermostat wirelessly communicates with andcooperates with the primary learning thermostat for improved control ofthe HVAC system, such as by providing additional temperature data at itsrespective location in the enclosure, providing additional occupancyinformation, providing an additional user interface for the user, and soforth.

It is to be appreciated that while certain embodiments are particularlyadvantageous where the thermostat 110 is a primary learning thermostatand the remote thermostat 112 is an auxiliary learning thermostat, thescope of the present teachings is not so limited. Thus, for example,while certain initial provisioning methods that automatically pair anetwork-connected thermostat with an online user account areparticularly advantageous where the thermostat is a primary learningthermostat, the methods are more generally applicable to scenariosinvolving primary non-learning thermostats, auxiliary learningthermostats, auxiliary non-learning thermostats, or other types ofnetwork-connected thermostats and/or network-connected sensors. By wayof further example, while certain graphical user interfaces for remotecontrol of a thermostat may be particularly advantageous where thethermostat is a primary learning thermostat, the methods are moregenerally applicable to scenarios involving primary non-learningthermostats, auxiliary learning thermostats, auxiliary non-learningthermostats, or other types of network-connected thermostats and/ornetwork-connected sensors. By way of even further example, while certainmethods for cooperative, battery-conserving information polling of athermostat by a remote cloud-based management server may be particularlyadvantageous where the thermostat is a primary learning thermostat, themethods are more generally applicable to scenarios involving primarynon-learning thermostats, auxiliary learning thermostats, auxiliarynon-learning thermostats, or other types of network-connectedthermostats and/or network-connected sensors.

Enclosure 100 further includes a private network accessible bothwirelessly and through wired connections and may also be referred to asa Local Area Network or LAN. Network devices on the private networkinclude a computer 124, thermostat 110 and remote thermostat 112 inaccordance with some embodiments of the present invention. In oneembodiment, the private network is implemented using an integratedrouter 122 that provides routing, wireless access point functionality,firewall and multiple wired connection ports for connecting to variouswired network devices, such as computer 124. Each device is assigned aprivate network address from the integrated router 122 eitherdynamically through a service like Dynamic Host Configuration Protocol(DHCP) or statically through actions of a network administrator. Theseprivate network addresses may be used to allow the devices tocommunicate with each directly over the LAN. Other embodiments mayinstead use multiple discrete switches, routers and other devices (notshown) to perform more other networking functions in addition tofunctions as provided by integrated router 122.

Integrated router 122 further provides network devices access to apublic network, such as the Internet, provided enclosure 100 has aconnection to the public network generally through a cable-modem, DSLmodem and an Internet service provider or provider of other publicnetwork service. Public networks like the Internet are sometimesreferred to as a Wide-Area Network or WAN. In the case of the Internet,a public address is assigned to a specific device allowing the device tobe addressed directly by other devices on the Internet. Because thesepublic addresses on the Internet are in limited supply, devices andcomputers on the private network often use a router device, likeintegrated router 122, to share a single public address through entriesin Network Address Translation (NAT) table. The router makes an entry inthe NAT table for each communication channel opened between a device onthe private network and a device, server, or service on the Internet. Apacket sent from a device on the private network initially has a“source” address containing the private network address of the sendingdevice and a “destination” address corresponding to the public networkaddress of the server or service on the Internet. As packets pass fromwithin the private network through the router, the router replaces the“source” address with the public network address of the router and a“source port” that references the entry in the NAT table. The server onthe Internet receiving the packet uses the “source” address and “sourceport” to send packets back to the router on the private network which inturn forwards the packets to the proper device on the private networkdoing a corresponding lookup on an entry in the NAT table.

Entries in the NAT table allow both the computer device 124 and thethermostat 110 to establish individual communication channels with athermostat management system (not shown) located on a public networksuch as the Internet. In accordance with some embodiments, a thermostatmanagement account on the thermostat management system enables acomputer device 124 in enclosure 100 to remotely access thermostat 110.The thermostat management system passes information from the computerdevice 124 over the Internet and back to thermostat 110 provided thethermostat management account is associated with or paired withthermostat 110. Accordingly, data collected by thermostat 110 alsopasses from the private network associated with enclosure 100 throughintegrated router 122 and to the thermostat management system over thepublic network. Other computer devices not in enclosure 100 such asSmartphones, laptops and tablet computers (not shown in FIG. 1) may alsocontrol thermostat 110 provided they have access to the public networkwhere the thermostat management system and thermostat management accountmay be accessed. Further details on accessing the public network, suchas the Internet, and remotely accessing a thermostat like thermostat 110in accordance with embodiments of the present invention is described infurther detail later herein.

In some embodiments, thermostat 110 may wirelessly communicate withremote thermostat 112 over the private network or through an ad hocnetwork formed directly with remote thermostat 112. During communicationwith remote thermostat 112, thermostat 110 may gather informationremotely from the user and from the environment detectable by the remotethermostat 112. For example, remote thermostat 112 may wirelesslycommunicate with the thermostat 110 providing user input from the remotelocation of remote thermostat 112 or may be used to display informationto a user, or both. Like thermostat 110, embodiments of remotethermostat 112 may also include sensors to gather data related tooccupancy, temperature, light and other environmental conditions. In analternate embodiment, remote thermostat 112 may also be located outsideof the enclosure 100.

FIG. 2 is a schematic diagram of an HVAC system controlled using athermostat designed in accordance with embodiments of the presentinvention. HVAC system 120 provides heating, cooling, ventilation,and/or air handling for an enclosure 100, such as a single-family homedepicted in FIG. 1. System 120 depicts a forced air type heating andcooling system, although according to other embodiments, other types ofHVAC systems could be used such as radiant heat based systems, heat-pumpbased systems, and others.

In heating, heating coils or elements 242 within air handler 240 providea source of heat using electricity or gas via line 236. Cool air isdrawn from the enclosure via return air duct 246 through filter 270,using fan 238 and is heated through heating coils or elements 242. Theheated air flows back into the enclosure at one or more locations viasupply air duct system 252 and supply air registers such as register250. In cooling, an outside compressor 230 passes a gas such as Freonthrough a set of heat exchanger coils 244 to cool the gas. The gas thengoes through line 232 to the cooling coils 234 in the air handler 240where it expands, cools, and cools the air being circulated via fan 238.A humidifier 254 may optionally be included in various embodiments thatreturns moisture to the air before it passes through duct system 252.Although not shown in FIG. 2, alternate embodiments of HVAC system 120may have other functionality such as venting air to and from theoutside, one or more dampers to control airflow within the duct system252 and an emergency heating unit. Overall operation of HVAC system 120is selectively actuated by control electronics 212 communicating withthermostat 110 over control wires 248.

Exemplary Thermostat Embodiments

FIGS. 3-7 and the descriptions in relation thereto provide exemplaryembodiments of thermostat hardware and/or software that can be used toimplement the specific embodiments of the appended claims. Thisthermostat hardware and/or software is not meant to be limiting, and ispresented to provide an enabling disclosure. FIG. 3 illustrates aperspective view of a thermostat 300, according to one embodiment. Inthis specific embodiment, the thermostat 300 can be controlled by atleast two types of user input, the first being a rotation of the outerring 312, and the second being an inward push on an outer cap 308 untilan audible and/or tactile “click” occurs. As used herein, these twotypes of user inputs, may be referred to as “manipulating” thethermostat. In other embodiments, manipulating the thermostat may alsoinclude pressing keys on a keypad, voice recognition commands, and/orany other type of input that can be used to change or adjust settings onthe thermostat 300.

For this embodiment, the outer cap 308 can comprise an assembly thatincludes the outer ring 312, a cover 314, an electronic display 316, anda metallic portion 324. Each of these elements, or the combination ofthese elements, may be referred to as a “housing” for the thermostat300. Simultaneously, each of these elements, or the combination of theseelements, may also form a user interface. The user interface mayspecifically include the electronic display 316. In FIG. 3, the userinterface 316 may be said to operate in an active display mode. Theactive display mode may include providing a backlight for the electronicdisplay 316. In other embodiments, the active display mode may increasethe intensity and/or light output of the electronic display 316 suchthat a user can easily see displayed settings of the thermostat 300,such as a current temperature, a setpoint temperature, an HVAC function,and/or the like. The active display mode may be contrasted with aninactive display mode (not shown). The inactive display mode can disablea backlight, reduce the amount of information displayed, lessen theintensity of the display, and/or altogether turn off the electronicdisplay 316, depending on the embodiment.

Depending on the settings of the thermostat 300, the active display modeand the inactive display mode of the electronic display 316 may also orinstead be characterized by the relative power usage of each mode. Inone embodiment, the active display mode may generally requiresubstantially more electrical power than the inactive display mode. Insome embodiments, different operating modes of the electronic display316 may instead be characterized completely by their power usage. Inthese embodiments, the different operating modes of the electronicdisplay 316 may be referred to as a first mode and a second mode, wherethe user interface requires more power when operating in the first modethan when operating in the second mode.

According to some embodiments the electronic display 316 may comprise adot-matrix layout (individually addressable) such that arbitrary shapescan be generated, rather than being a segmented layout. According tosome embodiments, a combination of dot-matrix layout and segmentedlayout is employed. According to some embodiments, electronic display316 may be a backlit color liquid crystal display (LCD). An example ofinformation displayed on the electronic display 316 is illustrated inFIG. 3, and includes central numerals 320 that are representative of acurrent setpoint temperature. According to some embodiments, metallicportion 324 can have a number of slot-like openings so as to facilitatethe use of a sensors 330, such as a passive infrared motion sensor(PIR), mounted beneath the slot-like openings.

According to some embodiments, the thermostat 300 can include additionalcomponents, such as a processing system 360, display driver 364, and awireless communications system 366. The processing system 360 canadapted or configured to cause the display driver 364 to cause theelectronic display 316 to display information to the user. Theprocessing system 360 can also be configured to receive user input viathe rotatable ring 312. These additional components, including theprocessing system 360, can be enclosed within the housing, as displayedin FIG. 3. These additional components are described in further detailherein below.

The processing system 360, according to some embodiments, is capable ofcarrying out the governance of the thermostat's operation. For example,processing system 360 can be further programmed and/or configured tomaintain and update a thermodynamic model for the enclosure in which theHVAC system is installed. According to some embodiments, the wirelesscommunications system 366 can be used to communicate with devices suchas personal computers, remote servers, handheld devices, smart phones,and/or other thermostats or HVAC system components. These communicationscan be peer-to-peer communications, communications through one or moreservers located on a private network, or and/or communications through acloud-based service.

Motion sensing as well as other techniques can be use used in thedetection and/or prediction of occupancy, as is described further in thecommonly assigned U.S. Ser. No. 13/632,070, supra. According to someembodiments, occupancy information can be a used in generating aneffective and efficient scheduled program. For example, an activeproximity sensor 370A can be provided to detect an approaching user byinfrared light reflection, and an ambient light sensor 370B can beprovided to sense visible light. The proximity sensor 370A can be usedin conjunction with a plurality of other sensors to detect proximity inthe range of about one meter so that the thermostat 300 can initiate“waking up” when the user is approaching the thermostat and prior to theuser touching the thermostat. Such use of proximity sensing is usefulfor enhancing the user experience by being “ready” for interaction assoon as, or very soon after the user is ready to interact with thethermostat. Further, the wake-up-on-proximity functionality also allowsfor energy savings within the thermostat by “sleeping” when no userinteraction is taking place or about to take place. The various types ofsensors that may be used, as well as the operation of the “wake up”function are described in much greater detail throughout the remainderof this disclosure.

In some embodiments, the thermostat can be physically and/orfunctionally divided into at least two different units. Throughout thisdisclosure, these two units can be referred to as a head unit and abackplate. FIG. 4 illustrates an exploded perspective view 400 of athermostat 408 having a head unit 410 and a backplate 412, according toone embodiment. Physically, this arrangement may be advantageous duringan installation process. In this embodiment, the backplate 412 can firstbe attached to a wall, and the HVAC wires can be attached to a pluralityof HVAC connectors on the backplate 412. Next, the head unit 410 can beconnected to the backplate 412 in order to complete the installation ofthe thermostat 408.

FIG. 5A illustrates an exploded perspective view 500 a of a head unit530 with respect to its primary components, according to one embodiment.Here, the head unit 530 may include an electronic display 560. Accordingto this embodiment, the electronic display 560 may comprise an LCDmodule. Furthermore, the head unit 530 may include a mounting assembly550 used to secure the primary components in a completely assembled headunit 530. The head unit 530 may further include a circuit board 540 thatcan be used to integrate various electronic components described furtherbelow. In this particular embodiment, the circuit board 540 of the headunit 530 can include a manipulation sensor 542 to detect usermanipulations of the thermostat. In embodiments using a rotatable ring,the manipulation sensor 542 may comprise an optical finger navigationmodule as illustrated in FIG. 5A. A rechargeable battery 544 may also beincluded in the assembly of the head unit 530. In one preferredembodiment, rechargeable battery 544 can be a Lithium-Ion battery, whichmay have a nominal voltage of 3.7 volts and a nominal capacity of 560mAh.

FIG. 5B illustrates an exploded perspective view 500 b of a backplate532 with respect to its primary components, according to one embodiment.The backplate 532 may include a frame 510 that can be used to mount,protect, or house a backplate circuit board 520. The backplate circuitboard 520 may be used to mount electronic components, including one ormore processing functions, and/or one or more HVAC wire connectors 522.The one or more HVAC wire connectors 522 may include integrated wireinsertion sensing circuitry configured to determine whether or not awire is mechanically and/or electrically connected to each of the one ormore HVAC wire connectors 522. In this particular embodiment, tworelatively large capacitors 524 are a part of power stealing circuitrythat can be mounted to the backplate circuit board 520. The powerstealing circuitry is discussed further herein below.

In addition to physical divisions within the thermostat that simplifyinstallation process, the thermostat may also be divided functionallybetween the head unit and the backplate. FIG. 6A illustrates asimplified functional block diagram 600 a for a head unit, according toone embodiment. The functions embodied by block diagram 600 a arelargely self-explanatory, and may be implemented using one or moreprocessing functions. As used herein, the term “processing function” mayrefer to any combination of hardware and/or software. For example, aprocessing function may include a microprocessor, a microcontroller,distributed processors, a lookup table, digital logic,logical/arithmetic functions implemented in analog circuitry, and/or thelike. A processing function may also be referred to as a processingsystem, a processing circuit, or simply a circuit.

In this embodiment, a processing function on the head unit may beimplemented by an ARM processor. The head unit processing function mayinterface with the electronic display 602, an audio system 604, and amanipulation sensor 606 as a part of a user interface 608. The head unitprocessing function may also facilitate wireless communications 610 byinterfacing with various wireless modules, such as a Wi-Fi module 612and/or a ZigBee module 614. Furthermore, the head unit processingfunction may be configured to control the core thermostat operations616, such as operating the HVAC system. The head unit processingfunction may further be configured to determine or sense occupancy 618of a physical location, and to determine building characteristics 620that can be used to determine time-to-temperature characteristics. Usingthe occupancy sensing 618, the processing function on the head unit mayalso be configured to learn and manage operational schedules 622, suchas diurnal heat and cooling schedules. A power management module 662 maybe used to interface with a corresponding power management module on theback plate, the rechargeable battery, and a power control circuit 664 onthe back plate.

Additionally, the head unit processing function may include and/or becommunicatively coupled to one or more memories. The one or morememories may include one or more sets of instructions that cause theprocessing function to operate as described above. The one or morememories may also include a sensor history and global state objects 624.The one or more memories may be integrated with the processing function,such as a flash memory or RAM memory available on many commercialmicroprocessors. The head unit processing function may also beconfigured to interface with a cloud management system 626, and may alsooperate to conserve energy wherever appropriate 628. An interface 632 toa backplate processing function 630 may also be included, and may beimplemented using a hardware connector.

FIG. 6B illustrates a simplified functional block diagram for abackplate, according to one embodiment. Using an interface 636 that ismatched to the interface 632 shown in FIG. 6A, the backplate processingfunction can communicate with the head unit processing function 638. Thebackplate processing function can include wire insertion sensing 640that is coupled to external circuitry 642 configured to provide signalsbased on different wire connection states. The backplate processingfunction may be configured to manage the HVAC switch actuation 644 bydriving power FET circuitry 646 to control the HVAC system.

The backplate processing function may also include a sensor pollinginterface 648 to interface with a plurality of sensors. In thisparticular embodiment, the plurality of sensors may include atemperature sensor, a humidity sensor, a PIR sensor, a proximity sensor,an ambient light sensor, and or other sensors not specifically listed.This list is not meant to be exhaustive. Other types of sensors may beused depending on the particular embodiment and application, such assound sensors, flame sensors, smoke detectors, and/or the like. Thesensor polling interface 648 may be communicatively coupled to a sensorreading memory 650. The sensor reading memory 650 can store sensorreadings and may be located internally or externally to amicrocontroller or microprocessor.

Finally, the backplate processing function can include a powermanagement unit 660 that is used to control various digital and/oranalog components integrated with the backplate and used to manage thepower system of the thermostat. Although one having skill in the artwill recognize many different implementations of a power managementsystem, the power management system of this particular embodiment caninclude a bootstrap regulator 662, a power stealing circuit 664, a buckconverter 666, and/or a battery controller 668.

FIG. 7 illustrates a simplified circuit diagram 700 of a system formanaging the power consumed by a thermostat, according to oneembodiment. The powering circuitry 710 comprises a full-wave bridgerectifier 720, a storage and waveform-smoothing bridge output capacitor722 (which can be, for example, on the order of 30 microfarads), a buckregulator circuit 724, a power-and-battery (PAB) regulation circuit 728,and a rechargeable lithium-ion battery 730. In conjunction with othercontrol circuitry including backplate power management circuitry 727,head unit power management circuitry 729, and the microcontroller 708,the powering circuitry 710 can be configured and adapted to have thecharacteristics and functionality described herein below. Description offurther details of the powering circuitry 710 and associated componentscan be found elsewhere in the instant disclosure and/or in U.S. Ser. No.13/467,025, supra.

By virtue of the configuration illustrated in FIG. 7, when there is a“C” wire presented upon installation, the powering circuitry 710operates as a relatively high-powered, rechargeable-battery-assistedAC-to-DC converting power supply. When there is not a “C” wirepresented, the powering circuitry 710 operates as a power-stealing,rechargeable-battery-assisted AC-to-DC converting power supply. Thepowering circuitry 710 generally serves to provide the voltage Vcc MAINthat is used by the various electrical components of the thermostat,which in one embodiment can be about 4.0 volts. For the case in whichthe “C” wire is present, there is no need to worry about accidentallytripping (as there is in inactive power stealing) or untripping (foractive power stealing) an HVAC call relay, and therefore relativelylarge amounts of power can be assumed to be available. Generally, thepower supplied by the “C” wire will be greater than the instantaneouspower required at any time by the remaining circuits in the thermostat.

However, a “C” wire will typically only be present in about 20% ofhomes. Therefore, the powering circuitry 710 may also be configured to“steal” power from one of the other HVAC wires in the absence of a “C”wire. As used herein, “inactive power stealing” refers to the powerstealing that is performed during periods in which there is no activecall in place based on the lead from which power is being stolen. Thus,for cases where it is the “Y” lead from which power is stolen, “inactivepower stealing” refers to the power stealing that is performed whenthere is no active cooling call in place. As used herein, “active powerstealing” refers to the power stealing that is performed during periodsin which there is an active call in place based on the lead from whichpower is being stolen. Thus, for cases where it is the “Y” lead fromwhich power is stolen, “active power stealing” refers to the powerstealing that is performed when there is an active cooling call inplace. During inactive or active power stealing, power can be stolenfrom a selected one of the available call relay wires. While a completedescription of the power stealing circuitry 710 can be found in thecommonly assigned applications that have been previously incorporatedherein by reference, the following brief explanation is sufficient forpurposes of this disclosure.

Some components in the thermostat, such as the head unit processingfunction, the user interface, and/or the electronic display may consumemore instantaneous power than can be provided by power stealing alone.When these more power-hungry components are actively operating, thepower supplied by power stealing can be supplemented with therechargeable battery 730. In other words, when the thermostat is engagedin operations, such as when the electronic display is in an activedisplay mode, power may be supplied by both power stealing and therechargeable battery 730. In order to preserve the power stored in therechargeable battery 730, and to give the rechargeable battery 730 anopportunity to recharge, some embodiments optimize the amount of timethat the head unit processing function and the electronic display areoperating in an active mode. In other words, it may be advantageous insome embodiments to keep the head unit processing function in a sleepmode or low power mode and to keep the electronic display in an inactivedisplay mode as long as possible without affecting the user experience.

When the head unit processing function and the electronic display are inan inactive or sleep mode, the power consumed by the thermostat isgenerally less than the power provided by power stealing. Therefore, thepower that is not consumed by the thermostat can be used to recharge therechargeable battery 730. In this embodiment, the backplate processingfunction 708 (MSP430) can be configured to monitor the environmentalsensors in a low-power mode, and then wake the head unit processingfunction 732 (AM3703) when needed to control the HVAC system, such as torecalculate an optimal control strategy as described herein. Similarly,the backplate processing function 708 can be used to monitor sensorsused to detect ambient temperature conditions, and wake the head unitprocessing system 732 and/or the electronic display when it isdetermined that a maintenance band threshold has been crossed and/or ananticipated wakeup time or event has occurred.

Stated differently, in accordance with the teachings of the commonlyassigned U.S. Ser. No. 13/467,025, supra, and others of the commonlyassigned incorporated applications, the thermostat described hereinrepresents an advanced, multi-sensing, microprocessor-controlledintelligent or “learning” thermostat that provides a rich combination ofprocessing capabilities, intuitive and visually pleasing userinterfaces, network connectivity, and energy-saving capabilities(including the presently described predictive control algorithms) whileat the same time not requiring a so-called “C-wire” from the HVAC systemor line power from a household wall plug, even though such advancedfunctionalities can require a greater instantaneous power draw than a“power-stealing” option (i.e., extracting smaller amounts of electricalpower from one or more HVAC call relays) can safely provide. By way ofexample, the head unit microprocessor can draw on the order of 250 mWwhen awake and processing, the LCD module (e.g., 560) can draw on theorder of 250 mW when active. Moreover, the Wi-Fi module (e.g., 612) candraw 250 mW when active, and needs to be active on a consistent basissuch as at a consistent 2% duty cycle in common scenarios. However, inorder to avoid falsely tripping the HVAC relays for a large number ofcommercially used HVAC systems, power-stealing circuitry is oftenlimited to power providing capacities on the order of 100 mW-200 mW,which would not be enough to supply the needed power for many commonscenarios.

The thermostat resolves such issues at least by virtue of the use of therechargeable battery (e.g., 544 (or equivalently capable onboard powerstorage medium)) that will recharge during time intervals in which thehardware power usage is less than what power stealing can safelyprovide, and that will discharge to provide the needed extra electricalpower during time intervals in which the hardware power usage is greaterthan what power stealing can safely provide. In order to operate in abattery-conscious manner that promotes reduced power usage and extendedservice life of the rechargeable battery, the thermostat is providedwith both (i) a relatively powerful and relatively power-intensive firstprocessor (such as a Texas Instruments AM3703 microprocessor) that iscapable of quickly performing more complex functions such as driving avisually pleasing user interface display, computing a parameterizedprediction model, applying the parameterized prediction model to a setof selected control strategies, minimizing a cost function to determinean optimal control strategy, and performing various other mathematicallearning computations, and (ii) a relatively less powerful and lesspower-intensive second processor (such as a Texas Instruments MSP430microcontroller) for performing less intensive tasks, including drivingand controlling the occupancy sensors, driving and controllingtemperature sensors, and the like. To conserve valuable power, the firstprocessor is maintained in a “sleep” state for extended periods of timeand is “woken up” only for occasions in which its capabilities areneeded, whereas the second processor is kept on more or lesscontinuously (although preferably slowing down or disabling certaininternal clocks for brief periodic intervals to conserve power) toperform its relatively low-power tasks. The first and second processorsare mutually configured such that the second processor can “wake” thefirst processor on the occurrence of certain events, such as sensing acondition that necessitates recalculating a predictive control strategy,which can be termed “wake-on” facilities. These wake-on facilities canbe turned on and turned off as part of different functional and/orpower-saving goals to be achieved. For example, an ambient temperaturesensor can be provided by which the second processor, when detecting anambient temperature or other condition that necessitates recalculating apredictive control strategy will “wake up” the first processor so thatit can perform one or more operations of the predictive controlalgorithm described herein or instruct the HVAC system to cycle betweenan on and off state.

It will be understood by one having skill in the art that the variousthermostat embodiments depicted and described in relation to FIGS. 3-7are merely exemplary and not meant to be limiting. Many other hardwareand/or software configurations may be used to implement a thermostat andthe various functions described herein below. These embodiments shouldbe seen as an exemplary platform in which the following embodiments canbe implemented to provide an enabling disclosure. Of course, thefollowing methods, systems, and/or software program products could alsobe implemented using different types of thermostats, different hardware,and/or different software.

FIG. 8A illustrates steps for a time to temperature computationaccording to an embodiment. As used herein, time to temperature (“T2T”)refers to an estimate of the time remaining from the current point intime until the target temperature will be reached. As described herein,the T2T information computed by the thermostat is specific to the heatedor cooled enclosure, or in other words, the determined T2T is tailoredto the enclosure. In view of the variety of factors that can affect thecourse of a temperature trajectory over a particular real-world HVACcycle, the methods described herein have been found to yield reasonablygood estimations. Moreover, in the face of the many real-worldvariations that can occur, some predictable and others not sopredictable, the currently described methods for selective display ofthe T2T information (for example, displaying “under 10 minutes” when theT2T time is getting small and not displaying the T2T information if itis “behaving” in an unexpected or unreliable manner) have been found toprovide pleasing overall user experiences with the T2T facility thatincrease the overall appeal and attractiveness of the thermostat suchthat the user will be drawn to engage further with its energy-savingfeatures and energy-conscious ecosystem. Notably, while the describedexamples are provided in the particular context of heating, the skilledartisan would readily be able to apply counterpart methods for thecooling context, which are thus within the scope of the presentteachings.

According to one preferred embodiment, the thermostat's T2T algorithm isfirst implicated by virtue of a learning phase (step 802) that occurssoon after first installation or factory reset, whereby the thermostatbegins to build and maintain its own database of T2T-relatedinformation, which is customized for that particular enclosure and thatparticular HVAC system, during the normal course of operation in a firstpredetermined number of “meaningful” or “non-maintenance” heatingcycles. By “non-maintenance” heating cycle, it is meant that there hasbeen an actual setpoint temperature change upon which the heating cyclewas instantiated. This can be contrasted with a “maintenance” heatingcycle, in which the setpoint temperature has remained the same but theHVAC system was activated due to a drop in temperature and operateduntil that temperature was again reached (maintained). In one example,the predetermined number of “learning” heating cycles is 10, althoughthis can be varied substantially without departing from the scope of thepresent teachings. For each such learning cycle, the thermostatautomatically (without requiring any affirmative instruction or teachingfrom the user) tracks the temperature change ΔH(t) versus time “t”,where t=0 represents the beginning of the heating cycle.

After a suitable number of learning cycles (step 804), there is built upa sufficient amount of data to automatically generate a historical model“G” of the enclosure, which can alternatively be termed a “global”model, that can be used to provide an initial estimate at the outset ofsubsequent T2T calculations. The global model can subsequently becontinuously improved using more data points as time goes forward, sinceeach heating cycle represents yet another “experiment” for thatenclosure to improve the “global model estimate,” which can also betermed a “historical model estimate.” For one preferred embodiment, thetime span of the global model can be limited to only a recent period,such as the most recent 30 to 60 days, so that it will be more likely toreflect the effects of the current season of the year.

FIG. 8B illustrates a conceptual diagram of the method of FIG. 8A,including a plot of the global model G. One mathematical function thathas been found to be convenient to compute, along with being reasonablysuitable, characterizes the global model as a single-parameter straightline (with linear parameter “c”) between ΔH=0 and ΔH=0.5 degrees C., andthen a two-parameter curve beyond that point (with linear and quadraticparameters “a” and “b”, respectively).

Referring now again to FIG. 8A, at step 808 the T2T algorithm is putinto use when the current operating setpoint temperature is changed froman initial value H₀ to a desired final value H_(F). This setpoint changecan be invoked by a user by using either the walk-up dial or a remotenetwork access facility, or alternatively can be when there is ascheduled setpoint change encountered that changes the current operatingsetpoint temperature. At step 812, an initial estimate T2T(0) iscomputed using only the global model G, by mapping the valueH_(F)−H₀=ΔH(0) into T2T(0) using the global model G as shown in FIG. 8B.This initial estimate, which can be called a global-model initialestimate, can be shown immediately on the thermostat display, even inreal time as the user turns the dial for the case of a manual walk-upsetpoint change.

At step 810, in what will usually last over the next several minutes ofthe heating cycle, a global-model estimate continues to be used toprovide the current time to temperature estimate TT(t), by virtue ofmapping the current measured room temperature H(t) into TT(t) using theglobal model G. The global model T2T estimate is denoted herein byTT_(G)(t). The actual room temperature values H(t) can be provided atregular periodic intervals, such as every 30 seconds, by the thermostatsensing circuitry. According to a preferred embodiment, during this timeperiod in which the global estimate is being used for display purposes,a real-time model R is being built up by virtue of tracking the currentvalue of ΔH(t)=H(t)−H₀ versus time. It has been found by the presentinventors that the real-time model R, which can alternatively be calleda “local” model, does not become useful for purposes of T2T estimationuntil such time as a reasonably straight line (statistically speaking)can be established, and that this straight line can often not beestablished until there has been a certain predeterminedempirically-established rise, such as 0.2 degrees C., at a point 854following a lowest point 852 in trajectory of H(t). Oneempirically-established guideline that has been found useful is to waituntil 10 temperature samples spaced 30 seconds apart subsequent to the0.2 degree C. post-nadir rise point 854 until a reasonably appropriateestimate can be computed using the real-time model. According to onepreferred embodiment, the real-time model R can be used to establish a“real-time model estimate” by using a straight-line projection onto thetarget temperature line, as shown in FIG. 8B. The real-time model T2Testimate is denoted herein by TT_(R)(t). For one embodiment, only thelatest 10 temperature samples (or other suitable number of recentsamples) are used to project the straight line that computes thereal-time estimate TT_(R)(t). In other embodiments, all of the datapoints subsequent to the point 854 can be used to compute the TT_(R)(t).

If at step 812 it is determined that the real-time model estimateTT_(R)(t) is not sufficiently reliable (e.g., using the above-describedcriterion of 10 points spaced 30 seconds apart following the point 854),then step 810 repeats until such time as TT_(R)(t) is sufficientlyreliable, whereupon step 814 is carried out. At step 814 there isinstantiated a transition between the global-model estimate TT_(G)(t)real-time model estimate TT_(R)(t), such that there is not a “jump” inthe actual value of TT(t), which can be disconcerting to a user who isviewing the display, the transition being summarized asTT(t)=TRANS[TT_(G)(t)→TT_(R)(t)]. The transition can be achieved in avariety of ways without departing from the scope of the presentteachings, but in one preferred embodiment is performed as astraight-line transition from one curve to the other, where thetransition occurs at a rate of not more than 10 seconds per second. Oncethe transition is complete, the real-time estimate alone can be used(step 816) until the end of the cycle.

As indicated in FIG. 8A, subsequent to the end of the cycle at step 816,there can be carried out a recomputation of the global model at step 806so that the most recent historical data can be leveraged prior toinstantiation of the next heating cycle. Alternatively, the global modelcan be recomputed once every several cycles, once per day, or on someother periodic basis.

Preferably, there are plural safeguards incorporated along with thesteps 814-816 such that “sanity” is retained in the displayed T2Testimate. If the safeguards indicate a state of unreliability or other“sanity” problem for the real-time model estimate, then the T2T displayis simply turned off, and instead of a time reading, there will simplyby the word HEATING (or the like) that is displayed. By way of example,if the statistical deviation of the data samples from a straight linesubsequent to point 854 is greater than a certain threshold, the T2Tdisplay is turned off. Likewise, if the real-time model estimate of T2Tstarts growing for an extended period of time, or indicates anunreasonably large number, the T2T display is turned off

Exemplary Predictive Control Systems

As shown in FIG. 9, conventional thermostats typically control a home'stemperature by defining a temperature or maintenance band 900 around adesired or set temperature 902 (referred to herein as a setpointtemperature). The maintenance band 900 typically is defined by an offsetvalue ΔT that defines an upper maintenance band threshold temperature904 and a lower maintenance band threshold temperature 906. A commonoffset value ΔT for such maintenance bands 900 is ±0.7 degrees from theset temperature. For example, is the setpoint temperature is 72°Fahrenheit, the upper maintenance band threshold 904 will beapproximately 72.7° Fahrenheit (e.g., 72+0.7) and the lower maintenanceband threshold 906 will be approximately 71.3° Fahrenheit (e.g.,72−0.7).

Common thermostat controls that use such maintenance bands are known asbang-bang controls. While the term ON-OFF control is also sometimes usedto describe such controls, the term bang-bang is used herein as beingmore descriptive than the relatively generic term ON-OFF. For a heatingoperation, these controls will cycle an HVAC system on when an ambienttemperature drops below the lower maintenance band threshold 906 andcycle an HVAC system off when the ambient temperature rises above theupper maintenance band threshold 904. The reverse is true for coolingoperations, namely, the HVAC system is cycled on when the ambienttemperature rises above the upper maintenance band threshold 904 and theHVAC system is cycled off when the ambient temperature drops below thelower maintenance band threshold 906. For example, using bang-bangcontrols for a heating operation and the above described 72° Fahrenheitsetpoint, the thermostat will cycle the HVAC system's heater on when theambient temperature drops below 71.3 degrees and cycle the heater offwhen the temperature rises above 72.7 degrees.

Bang-bang controls are very reactive in nature since they cycle HVACsystems on and off only when the ambient temperature band crosses adefined threshold (i.e., the upper and lower maintenance bands).Further, these controls do not account for thermal mass and thermalinertia in an enclosure, which as described herein may lead tosignificant overshooting and/or undershooting. As used herein, the term“thermal inertia” refers to a speed with which a material or body'stemperature will equalize with a surrounding temperature. Thermalinertia is a bulk material property that is related to a material'sthermal conductivity and volumetric heat capacity and is often dependentupon its absorptivity, specific heat, thermal conductivity, dimensions,and the like.

As used herein, the term “system inertia,” while being related tothermal inertia, refers in a more general sense to the speed at whichthe ambient temperature of a home (or other enclosure) will actuallyrespond to the activation of a heating or cooling cycle for the home (orother enclosure). System inertia can take into account the nature of theheating or cooling equipment itself and the manner in which heat istransferred into the home. Thus, by way of example, a particular homehaving a radiant heating system may exhibit a relatively large systeminertia (heats up slowly) if the radiant heating system is low-poweredor weak, whereas that same home may exhibit a much smaller systeminertia (heats up quickly) if that radiant heating system were replacedby a much more powerful one. When viewing the home and its HVAC systemas a control system in which the input is the on/off state of the HVACsystem and the output is the ambient temperature that the occupantfeels, the system inertia can be seen as a static or quasi-staticelement of the model for that control system.

An example of the effects of relatively high system inertia is evidentin many radiant heating systems for homes where the floor of the home isoften heated and the surrounding air is heated via radiation andconvection from the heated floor, with radiation typically being thedominant mode of heat transfer. The heating of the ambient air can oftentake place rather slowly, due to both the fact that it often takessubstantial time for the floor itself to heat up as well as the factthat the radiant heat transfer modality from the floor into the air alsotakes time. The continued heat radiation causes the home's temperatureto continue to rise or “overshoot” the setpoint temperature, sometimeswell above the setpoint temperature, which can cause discomfort tooccupants. A similar effect is evident when heating is again performedas the floor must be heated before heating the surrounding air. Thethermal inertia of the floor or home causes the floor's temperature totemporarily drop while the floor is being heated or “undershoot” thesetpoint temperature, which can likewise cause discomfort.

Embodiments of the invention include predictive controls or modelpredictive controls for heating and/or cooling a home. These controlsare different than conventional bang-bang controls where a heating orcooling operation is performed only when a temperature rises or fallsoutside defined a maintenance band window. Using predictive controls, aheating or cooling operation may be discontinued or engaged even whenthe ambient temperature is within the temperature band window (i.e., theambient temperature has not crossed a maintenance band threshold) oreven when an ambient temperature has not entered a maintenance bandwindow.

The methods and systems described herein are generally directed towardHVAC systems characterized by a relatively high system inertia, such asmany radiant heating systems, although the methods and systems maylikewise be used for other types of systems that may exhibit behaviorsor symptoms, either on a temporary, seasonal, or permanent basis, ofhaving a high system inertia. Likewise, although the methods and systemsdescribed herein are directed mainly toward radiant heating systems, themethods and systems described herein may be apply equally to radiantcooling systems or other types of heating or cooling systems. Thus,while the terms “radiant heat”, “radiant system”, and the like are usedin the description below for purposes of clarity of description, it isto be appreciated that the scope of the present teachings is not solimited.

According to some embodiments, provided is a smart radiant heatingcontrol mode for the home HVAC system that is carried out by anintelligent, multi-sensing thermostat. When it has been established dueto automated sensing and/or affirmative user input (see FIG. 22, infra,and associated automated “system match” discussion) that smart radiantheat control is to be invoked, the thermostatic control of the heatingmodality proceeds according to a predictive control algorithm that isjudiciously invoked according to an availability of parameters for apredetermined home heating system model in which sufficient confidencehas been established. For one embodiment, the parameters for the systemmodel is based solely on data collected from historical heating cyclesin which the radiant heating system was used. For other embodiments,other factors such as time of day, outside temperatures, windconditions, solar heating angles, orientation of the home and windowsrelative to the sun, and/or any of a variety of other relevantinformation can be used in determining the parameters. For purposes ofensuring a smooth, consistent, pleasant occupant experience, thepredictive control algorithm is only invoked when sufficient confidencehas been established in the home heating system model, or whensufficient confidence has been regained after having been lost due torecent anomalous or partially anomalous measured events. By an anomalousor partially anomalous event, it is meant that something occurred withthe data used to compute the heating system model that caused a modelconfidence metric to deteriorate, such as a door or window is left openfor an extended time period during winter, data loss events, powerfailures, extraordinary weather conditions, and the like.

When not operating in the predictive control mode, the smart radiantcontrol algorithm operates according to a modified version of abang-bang control mode that is designed to be substantially moreaggressive toward reduction of overshoot. More particularly, theaggressive overshoot reduction method comprises bang-bang control of theambient temperature to within a particular maintenance band, termedherein an “aggressive overshoot reduction maintenance band”, of thecurrent setpoint temperature in which both the upper maintenance bandtemperature and the lower maintenance band temperature lie below thecurrent setpoint temperature. Thus, while conventional thermostaticbang-bang temperature control for a setpoint temperature of “T” maymaintain a symmetric temperature band of (T−ΔT) to (T+ΔT) around thesetpoint temperature T, and while moderate overshoot-reducing bang-bangtemperature control may maintain an asymmetric temperature band of(T−Δ1) to (T+ΔT2), where ΔT1>ΔT2>0, the currently described aggressiveovershoot reduction method comprises bang-bang control to within anasymmetrically offset maintenance band of (T−ΔT3) to (T−ΔT4), whereΔT3>ΔT4>0. By way of example only and not by way of limitation, for atypical setpoint temperature of 72 degrees F., the values for ΔT3 andΔT4 may be 1.0 degree F. and 0.5 degree F., respectively.

The use of aggressive overshoot-reducing bang-bang control as a“fallback” in the event that sufficient confidence has not beenestablished (or has been lost) in the system model for predictivecontrol has been found to provide a more beneficial user experience thanthe use of traditional bang-bang control. Nevertheless, the scope of thepreferred embodiments is not so limited, and in other embodiments the“fallback” can be the use of a symmetric or moderately asymmetricbang-bang control maintenance band around the setpoint temperature.

For one preferred embodiment, it has been found particularly effectiveto incorporate a sort of “hysteresis” around the invocation ofpredictive control versus a non-predictive or “fallback” control method.By way of example, if operating in a non-predictive mode, it is requiredthat at least two consecutive days of model parameter confidence beestablished before invoking the predictive control mode. Likewise, ifoperating in a predictive control mode, it is required that at least twoconsecutive days of model parameter non-confidence be established beforeinvoking the non-predictive control mode. Advantageously, this sort of“hysteresis” around the selective invocation of predictive control modefurther enhances the continuity of experience that is felt by the homeoccupants.

Operation of the smart radiant heat algorithm while in predictivecontrol mode according to some embodiments is now described. Toimplement the predictive controls, a system may be configured to performa “predictive control algorithm”. For example, the thermostat'sprocessing system may access memory having the predictive controlalgorithm stored thereon and may perform one or more of the processes,computations, and the like described hereinbelow. In one embodiment,some or all of the processes, computations, and the like, are performedby the relatively high-power consuming processor of the head unit withthe processor is in the active or awake operational mode. In anotherembodiment, some or all of the processes, computations, and the like,are performed by the relatively low-power consuming backplate processor.In other embodiments, one or more of the processes, computations, andthe like, are shared between the head unit processor and the backplateprocessor and/or information is shared therebetween. For convenience indescribing the various embodiments, the description will be directedmainly toward the predictive control algorithm.

In some embodiments, the predictive control algorithm may determine ifthe predictive control features are appropriate for the home'sthermostat system. For example, the predictive control algorithm maydetermine if undershooting or overshooting is occurring and/or by whatamount. In some radiant heating situations, the ambient temperature mayquickly begin to rise when a heating cycle begins and may quickly dropwhen the heat cycling is terminated. If undershooting and/orovershooting is not a considerable problem, the predictive controlalgorithm may determine that the predictive control features describedherein are not needed. In such embodiments, the predictive controlalgorithm may determine if predictive controls are even necessary andadjust an “on/off” setting of the thermostat accordingly.

It is to be appreciated that, while a system model that includes a “Lag”parameter is set forth below to represent one particular way that aradiant heating system might be characterized to a reliable degree uponcollection of historical radiant heating performance data by theintelligent thermostat, any of a variety of different modeling methodshaving any of a variety of different degrees of complexity and types ofmodeling parameters can be used without departing from the scope of thepresent teachings. For example, while the “Lag” parameter describedhereinbelow represents a sort of “hybrid” between (i) a static orquasi-static parameter representative of the system inertia of the homeand its HVAC system, and (ii) a dynamic, time-dependent, and/orcondition-dependent parameter that could depend on various factors suchas time of day, season, outside temperature, solar radiationimplications, and so forth, it is certainly within the scope of thepresent teachings to model the home heating system using more ordifferent parameters such that (a) static or quasi-static properties ofthe enclosure/HVAC system are captured and maintained, and (b) multipledynamic parameters representative of more dynamic, time-dependent,and/or condition-dependent parameter are separately captured,maintained, and used for the appropriate combination of times andconditions.

To determine if the predictive controls are needed, the predictivecontrol algorithm may calculate or measure an enclosure or system'sinertia. Measuring a system's inertia refer generally to capturing one,two, or more characteristic constants, that represent dynamics of thehouse. These constants could be identified using system identificationtechniques, such as the ones described herein. In one embodiment, thepredictive control algorithm may calculate or measure an enclosure's“Lag”. The term Lag refers to the time required to raise the temperatureof the enclosure by a defined amount (e.g., 0.5° Fahrenheit, 3°Fahrenheit, 5° Fahrenheit, and the like) after applying heat and is asimple representation that at least partially takes into account theenclosure's thermal inertia, although there are also somedynamic/condition-dependent components to it as well. In someembodiments, Lag measurements may not be calculated unless the HVACsystem has been off for a defined period, such as 60 seconds, to ensurethat no residual heat remains in the enclosure from previous heatingcycles. Similarly, the HVAC system may be required to stay on for apredefined time before a Lag measurement is recorded, such as until theenclosure's ambient temperature rises by the defined amount (e.g., 5°Fahrenheit). If the HVAC system cycles off before the enclosure'sambient temperature rises by this amount, the Lag measurement may bediscarded.

Several Lag measurements may also be recorded at defined periodsthroughout the day to allow the predictive control algorithm to accountfor temperature rises or drops that may be due to environmental factorssuch as exposure to sun, rain, overcast conditions, and the like.According to one embodiment, a day may be divided into equal timeperiods (e.g., 6 hour increments) that represent a pre-dawn period(e.g., 12 a.m. to 6 a.m.), a morning period (e.g., 6 a.m. to 12 p.m.),an afternoon period (e.g., 12 p.m. to 6 p.m.) and an evening period(e.g., 6 p.m. to 12 a.m.). Lag measurement may be recorded during eachof these time periods, and in some embodiments, an average Lag value maybe calculated from the various time period Lag measurements. The averageLag value may be used in implementing the predictive control methodsdescribed herein. According to another embodiment, an average Lag valuemay be determined for each of the time periods. The time period specificLag value may subsequently be used in implementing the describedpredictive control methods in order to obtain a more preciseapproximation of the radiant heating effects.

For example, an evening period Lag value may be significantly largerthan a morning period Lag value. In some embodiments, the Lagmeasurements may be taken even if the predictive control feature isdisabled. Accordingly, the average Lag values may be available for useupon the user enabling the predictive control feature. In otherembodiments, such as when the thermostat is newly installed, the systemmay be required to run for a defined time period (e.g., 1 week) beforethe predictive control feature is available in order to allow Lag valuesto be measured and recorded and an average value to be calculated.

The average Lag value may be modified, adjusted, and/or updated atdefined time periods as the system adjusts to the specific heatingproperties of the enclosure. For example, the system can continuallymeasure and record Lag values and modify or adjust an average Lag valueto more approximately model the enclosure. This process may be donemonthly, weekly, nightly, and the like. More recently measured Lagvalues may be weighted so that they influence the average Lag valuemore. In this manner, the Lag value may approximately match currentconditions for the enclosure.

Referring now to FIG. 10, in predicting a temperature response to aradiant heating operation, the predictive control algorithm may evaluatea set of candidate control strategies (hereinafter control strategies)that may be used in controlling the radiant heating system (see FIGS.17-19). Each control strategy may include a plurality of binary-valuedtime steps t₁−t_(n) (i.e., 1 or 0) that define when an HVAC system iscycled on and off (hereinafter time steps or control intervals). Thetime steps t₁−t_(n) may have a time or duration interval, which in someembodiments is approximately 5 minutes, 10 minutes, 15 minutes, 20minutes, and the like, although 10 minute duration may be preferred tominimize noise and/or head unit wake up.

The binary-valued control strategies may have an overall on-timepercentage, which refers to a percentage of time the HVAC system iscycled on during a “predefined control duration” or a total duration forthe control strategy (hereinafter control strategy duration). Forexample, if the control strategy duration is approximately 1 hour andthe HVAC system is cycled on for 30 minutes, the overall on-timepercentage would be approximately ½. The control strategy duration maybe approximately 30 minutes, 1 hour, 2 hours, and the like, although aduration of 1 hour may be preferred to minimize noise and/or head unitwake up. As described in more detail below, each control strategy may beconstrained to have a minimum number of on-time cycles that achieve thecandidate overall on-time percentage (e.g., 1 on-time cycle for a ⅙^(th)on-time percentage, 2 on-time cycles for a ⅓^(rd) on-time percentage, 3on-time cycles for a ½ on-time percentage, and the like). The controlstrategy may also define a “control trajectory” or ambient temperaturetrajectory 1004-1010, which refers to a predicted trajectory of anambient temperature of the enclosure due to a respective defined controlstrategy heating operation.

Based on the determined average Lag values, a simple temperatureprediction 1000 may be calculated for an HVAC system based on each ofthe control strategies. For example, a simple temperature prediction maybe calculated for each time step of one or more control strategies. Thecontrol or ambient temperature trajectory 1004-1010 may then bedetermined based on the predicted temperatures for each of the controlstrategies. The ambient temperature trajectory 1004-1010 for eachcontrol strategy may be evaluated or processed in view of apredetermined assessment functions (hereinafter a cost function) toselect an optimal control strategy. The optimal control strategy may beselected according to one or more predetermined assessment criteria,such as minimizing a cost value as described below. Minimizing a costvalue may essentially involve determining which ambient temperaturetrajectory 1004-1010 has the least amount of total variance V from asetpoint temperature ST.

The cost value may be calculated based on the difference between thesetpoint or target temperature ST and predicted temperature of each timestep t₁−t_(n) of a given control strategy. According to someembodiments, the cost value for the control strategy may be the sum ofthe target temperature minus the predicted temperature at each time stept₁−t_(n) squared as shown below:

${Cost} = {\sum\limits_{0}^{X}\left( {{T(k)}_{target} - {T(k)}_{predicted}} \right)^{2}}$

As described below, the cost function for multiple control strategiesmay be calculated and the control strategy with the lowest value may beselected as the most appropriate control strategy to use to heat theenclosure. Stated differently, the cost function may be minimized todetermine the most appropriate heating operation to perform. In someembodiments, the cost function may be weighted so that the calculatedcost value is more heavily influenced by future time step temperaturepredictions that are more likely to be closer to or farther away fromthe setpoint or target temperature ST and, thus, more likely to estimateovershooting or undershooting. For example, a weight factor varyingbetween 0 and 1 (Wt (k)) for time step k may be multiplied by thedifference between the target temperature ST and predicted temperaturesquared at time step k. Thus, control strategies having futuretemperatures with larger target and predicted temperatures variances Vwill have a greater cost and less likelihood of being selected.

${Cost} = {\sum\limits_{0}^{X}{{{Wt}(k)}\left( {{T(k)}_{target} - {T(k)}_{predicted}} \right)^{2}}}$

The minimized cost function may approximate the least amount ofovershooting and undershooting to occur for a given control strategysince overshooting and undershooting will be reflected in the costequation by the difference in the predicted and target temperatures ST.As stated above, future time steps t₁−t_(n) are more likely to representundershooting or overshooting and may be appropriately weighted toinfluence the cost function. In some embodiments, such as when thesystem does not have enough heating data for a home or the Lag value isvery short (e.g., less than 10 or 20 minutes), minimizing the costfunction may result in a prediction that predictive controls should notbe used. In such embodiments, the system may switch to conventionalbang-bang controls and a conventional maintenance band and offset value(e.g., ±0.7 degrees) may be used.

Prediction Model

To predict the temperatures at each of the time steps describedpreviously, a parameterized model (hereinafter prediction model) may beused that predicts a temperature change dT(i) for each time step. Theprediction model may be based on historical ambient temperatures for theenclosure acquired by the thermostat during associated historicalperiods in which radiant heat control was actuated. This historical datamay be stored on various memory device including both internal andexternal (i.e., cloud) devices or servied. For example, in oneembodiment, the prediction model is based on a regression analysis ofone or more independent variables, with the temperature change dT(i)being the dependent variable. In a specific embodiment, the regressionanalysis may use two independent variables (e.g., radiant heat and solarradiance), although in other embodiments, 3 or more independent variablemay be used (e.g., outside temperature, humidity, and the like). Anexemplary prediction model is shown below.

dT(i)=a0+a1*SR(i)+a2*Σ(Activation(k)*u _(—heat(i−k)))

In the above equation, the a0, a1, and a2 are regression coefficientsthat are obtained by a least square fit of historical numerical datapoints, which calculation may be conducted at repeated intervals, suchas every month, every week, every day (e.g., midnight), and the like.The data points may include, among other things, the temperature T, thechange in temperature dT, the heat applied u_heat, and the like, whichmay be recorded at each time step within a defined period, such aswithin 24 hours, 1 week, 1 month, and the like. A least square fit ofthese historical data points may be obtained to determine the regressioncoefficients (i.e., a0, a1, and a2). In some embodiments, the historicaldata and least square fit calculation may be configured to more heavilyweigh or consider data points that were recently obtained (e.g., datapoints obtained within the last week or several days). As described inmore detail below, the prediction model includes predetermined responsetrajectories (hereinafter functions), such as SR(i) for solar radiation,Activation(k) for an activation function, and u_heat for a heat inputresponse. These functions are weighted by the regression coefficients,which are calculated by fitting a model on historical data. Theregression coefficients may increase as the measured significance of thecorresponding function increases or decrease as the measuredsignificance of the corresponding function decreases.

According to the above prediction model, the regression coefficient a0represents a constant, which is typically a negative value to show thatin the absence of heating factors (e.g., solar radiance SR and radiantheating), the change in temperature dT will be negative. Stateddifferently, a negative regression coefficient a0 ensures that theestimated temperature T will drop in the absence of a heat source aswould be expected. SR(i) represents a function that approximates oraccounts for solar irradiance as described below. Activation(k)represents a function that weighs or accounts for previous radiantheating inputs (u_heat) in predicting a current change in temperature.According to this model, previous radiant heating inputs (u_heat(i−Lag))typically have the most effect on temperature change dT(i). In someembodiments, the current heating input, u_heat(i) and the oldest heatinginput, u_heat(i−2*Lag) may have less of an effect on temperature changedT(i), although the model may be adjusted if these inputs are determinedto be more significant. k is the time length of the activation function,which may range from 1 to 2*Lag as described in the model below, or mayvary depending on the model used. Activation(k) smoothens the heaterinputs (u_heat), while delaying its effect, which may simulate thethermal mass or inertia of the enclosure. From the above temperaturechange equation dT(i), the temperature T at any given time step may bedetermined from the equation below.

T(i+1)=T(i)+dT(i)

In this equation, T(i) is the temperature at time step i. dT(i) is thepredicted changing in temperature at any time step i. T(i+1) is apredicted temperature at a subsequent time step, which is equal to thetemperature at time step i plus the change in temperature dT(i).According to one embodiment, the time step duration (e.g., i, i+1, etc.)may be about 10 minutes. 10 minutes time step intervals have beendetermined to reduce noise in the calculation while reducing power andcalculation requirements and allowing the head unit to remain asleep.

The solar radiation may be modeled as a curve as shown in FIG. 11. Thesolar radiation may be modeled to predict how much the solar radiationaffects the enclosure's ambient temperature. For example, between aperiod of 0-6 hours (e.g., midnight to 6 a.m.) the modeled effects ofsolar radiation are zero to show that the sun has not yet risen and,thus, the enclosure is not yet affected by solar radiance. Between theperiod of 6-18 hours, the model rises from zero to one and back to zeroshowing that as the sun travels overhead, the solar radiance becomesmore intense and then less intense on the enclosure (a solar radiance of1 represents full solar radiance), which predictively leads to anincrease in ambient temperature. Between the period of 18-24 hours, themodeled effects are once again zero to show that the sun has set.

As shown in the solar radiance model, the curve rises and falls sharplyand tapers toward the middle, which implies that radiance effects may befelt quickly in the day (e.g., by 10 a.m.) and felt roughly throughoutthe day. The shape of the solar radiance model may be adjusted to morefully represent the location of the home. For example, the model curvemay have different shapes or be skewed to show relatively stronger solarradiance effects in the morning or evening. The solar radiance hours maybe extended based on the latitude or longitude of the location, and thelike. In one embodiment, the temperature rise of an enclosure may bemonitored and measured throughout the day to establish and/or adjust asolar radiance model and thereby tailor the model to the specificenclosure. In this manner, the solar radiance model may be unique to thespecific enclosure and/or location to account for trees, landscaping,surrounding homes or buildings, and the like, that may affect solarradiance temperatures. The solar radiance model may also be dependent oncurrent environmental conditions or the time of year, such as if cloudcover is present, if it is raining or snowing, or if the heatingoperation is occurring in the fall or winter. In other embodiments,solar radiation effects may be modeled with other shapes including atriangle, and the like.

Similarly, the activation function described above may be modeled toshow and facilitate calculating the effects of radiant heater inputsbefore and/or after a given time step. For example, as shown in FIG. 12,the activation function may be modeled as a triangle showing theincrease in temperature effects resulting from operation of the radiantheater. In other embodiments, the activation function may be modeled asvarious other shapes including curves, parabolas, Gaussian, and thelike. These additional shapes may be dependent on the specific heatedenclosure, the location of the enclosure, and the like, and may moreprecisely capture the thermal mass or inertial effects for theenclosure.

In some embodiments, the modeled activation function may include a rangeof up to two times the Lag value to account for residual heating effectsthat occur after radiant heating is discontinued. For example, FIG. 12is modeled for a system with a Lag of approximately 60 minutes and timesteps of 10 to 20 minutes (the model illustrates 20 minute intervals).The model shows that when radiant heating is started (i.e., time=0 ork=0) no radiant heating effects are felt (i.e., activation of 0). After60 minutes (i.e., k=60) the full effects of radiant heating are felt(i.e., activation of 1) since the time is equivalent to the Lag value.Time periods longer than 60 minutes (i.e., 60 minutes to 120 minutes)represent residual effects of radiant heating that occur due to systeminertia. An activation length of 120 minutes (i.e., k=120) returns k tozero as at this point predictively no radiant heating is occurring. Agraph showing modeled effects of Lag-delayed heating versus a radiantheater state based on activation function of FIG. 12 is shown in FIG.13, which shows that heating effects continue to be felt even after aradiant heater is turned off.

In effect, the summation function shown above is a convolution of heaterinput signals and the activation function model, which predicts theimpacts of radiant heating at any given time step. The summationfunction may be in effect a simple autoregressive moving average modelthat does not require the calculation or storage of large amount ofprevious radiant heat data, which otherwise may require largecomputation and/or power requirements that may not be available on apower limited thermostat. The Lag value in effect may convolve theprevious radiant heat inputs into a single value, which makes thesummation function more manageable to calculate and reduces the powerrequirements.

As mentioned previously, the activation model is dependent on the Lagthat is calculated for the heated enclosure as described above and,thus, a value of 60 minutes for Lag is merely illustrative and willtypically vary according to the individual heated enclosurecharacteristics. Also, as described above, the Lag value may varydepending on the time of day or one or more other environmental orenclosure conditions. As such, the activation function may be specificto the time of day or one or more other environmental or enclosureconditions. Similarly, the activation function need not be triangular inshape and may comprise various other shapes that may provide a betterapproximation of the enclosure.

FIG. 14 represents the above equation in control system form. As shownin FIG. 14, in some embodiments, the above described equation mayinclude a third variable (Tout) that represents the effects of theoutside temperature, such as due to thermal transfer through the walls,which may be weighted by an additional regression coefficient in amanner similar to that described herein. As can be appreciated by thoseskilled in the art, the regression coefficients (e.g., a0, a1, a2, andthe like) that are obtained via a least square fit to approximate theheating effects on specific enclosures. For example, if the enclosure'sinsulation is good, the corresponding coefficients for solar radianceand/or outside temperature will be low, showing that the enclosure'sheat change is due mainly to radiant heating. Similar results wouldoccur if the solar radiance or outside temperature were determined tohave a greater or more significant impact on the enclosure'stemperature. The above equation allows the predictive controls to betuned to the specific enclosure being heated. Although not shown, themodel could include other variables to account for humidity, rain, snow,elevation, and the like, which may each be weighted depending on thesignificance of the variable. The enclosure could also averagetemperature readings from various sensors positioned within theenclosure. For example, radiant heat often radiates from the floor. Assuch, floor sensors may be used to determine when the floor is close tothe setpoint temperature in order to turn the thermostat on and off

FIG. 15 shows a calculation of the temperature variation in an enclosureusing the above described prediction model equation. 1502 represents thetemperature swing in the enclosure, 1504 represents the radiant heatapplied, 1506 represents the delayed or lag-compensated heat, which is aconvolution of the applied heat and the activation function. 1508represents the solar radiance heat effect.

As previously described, the predictive model may be “fit” with thehistorical numerical data points on a periodic basis, such as everymidnight, to obtain the regression coefficients a0, a1, a2, and thelike. A “goodness of fit” may also be calculated for the predictivemodel, which represents the how well the model represents the variancein temperature for the enclosure. The goodness of fit typically variesbetween 0 and 1 with higher numbers representing a closer fit of themodel with the historical data. In some embodiments, the predictivemodel may only be used if the goodness of fit is above some minimalthreshold, such as above 0.5. If the goodness of fit is below thisminimal threshold, the system may default to conventional bang-bangcontrols. Additional checks may be performed on the predictive model andthe historical data points. For example, a sign of the coefficient forsolar radiation (i.e., ±) may be checked to ensure that the sign ispositive, which implies a temperature rise due to solar radiation. Ifthe sign is negative, which would imply a temperature drop due to solarradiation effects, the model may be rejected. Outlier data points of thehistorical data may also be detected and rejected in some embodiments.

FIG. 16 shows a histogram of the calculated fit for approximately 600thermostat devices over a two month period (December and January). Thethermostats were operating heaters with a lag of 30 minutes or more andat least 2 weeks of contiguous data was considered. As shown in thehistogram, 75% of the considered devices achieved the 0.5 minimumthreshold, which indicates improved heating operations using thedescribed predictive controls. In other embodiments, the goodness of fitmay be used to determine the width of the maintenance band as describedbelow.

The above equation simplifies more complex predictive control equations,such as an ARMA (autoregressive moving average) equation, into anequation that can easily be programmed on the power limited thermostatdevice. It also vastly reduces the computing power required to make thetemperature change prediction. Further, the Lag factor reduces theoverall number of inputs that must be considered to make a temperaturechange prediction by combining previous heat input effects into a singlevariable, which would otherwise need to be individually computed.

Control Algorithm

The control algorithm uses the above described predictive model topredict the temperatures at each of the time steps. The controlalgorithm also determines a control strategy based on a minimization ofa cost function as previously described. In essence, the controlalgorithm calculates both an amount of time for the HVAC system toremain on and a time for the head unit to wake up to reassess itsoperating state or condition. By iteratively running the predictionmodel over a defined amount of time steps or control intervals (e.g., 6time steps over 60 minutes), a control strategy may be chosen, such thatit minimizes overshoots and/or undershoots, while keeping the HVAC statechanges to a minimum. The chosen control strategy is implemented and theprocess repeats every time the head unit wakes up to reevaluate thecontrol strategies. Accordingly, even if an incorrect prediction is madeand a less than desirable control strategy is chosen, the system mayreevaluate its situation and correct the control strategy when it wakesup, which may be due to an anticipated wake up time, crossing amaintenance band, activation of a proximity sensors, or for some otherreason.

In some embodiments, the number of control strategies that arecalculated or predicted may be reduced to a defined subset (i.e., a setof candidate control strategies). The defined subset of controlstrategies considered may further depend on whether the ambienttemperature is below the lower maintenance band threshold, above theupper maintenance band threshold, or within the maintenance band. Thedefined subset of control strategies may minimize the number of timesthe radiant heater is cycled on and off, which is preferred when usingradiant heating systems. Specifically, the defined subset of controlstrategies may be selected so that only a single state or transitionoccurs in a given sequence of heater control actions, or so that theradiant heating system does not cycle on more than twice in a givencontrol duration. A limit may also be placed on how quickly the state ofthe heater can transition from one state to another. For example, in oneembodiment, the heater may be required to remain on or off for 20minutes or more to reduce on/off cycle transitions. The defined subsetof control strategies may be further reduced based on the limit of howquickly the system may transition between on/off states as describedbelow. The defined subset of control strategies further minimizes thecomputations required and, thus, reduces the computational and/or powerrequirements for the control system.

FIG. 17 illustrates a defined subset of control strategies (i.e., a setof candidate control strategies) that may be considered when the ambienttemperature is below a lower maintenance band threshold. Specifically,FIG. 17 shows 6 control strategies that may be considered, although moreor less control strategies may be considered in other embodiments. Thecontrol strategies are binary-valued, meaning that the inputs are either1 or 0 where an input of 1 represents an HVAC on-time cycle and an inputof 0 represents an HVAC off-time cycle. Accordingly, the first controlstrategy considers a single heater on-time cycle at a first time step(i.e., i) followed by five consecutive off-time cycle. The first controlstrategy has an overall on-time percentage of approximately ⅙. Incontrast, the sixth control strategy considers six consecutive heateron-time cycles at six consecutive time steps (i.e., time step i to i+5)for an overall on-time percentage of approximately 1. The interveningcontrol strategies (i.e., 2-5) each consider a single additional heateron-time cycle for an additional time step relative to the previouscontrol strategy. As described above, the time steps may be some definedperiod of time, such as 5 minutes, 10 minutes, 20 minutes, and the like.

According to one embodiment, the defined subset of control strategiesmay be further reduced based on a defined limit as to how quickly theheater may transition between on-time and off-time cycles. For example,if the time steps are defined as 10 minutes and the heater is limited tomaintaining a current cycle for at least 20 minutes, the first controlstrategy will not be considered in the cost minimization function unlessthe heater was already on.

The cost function described above, which may or may not include theweight factor, may be minimized to determine the most appropriatecontrol strategy to use. Based on the chosen control strategy, thecurrent heater state is determined (i.e., on-time or off-time cycle) anda wake up time for the head unit is determined based on the time stepduration and the number of time steps required until a state transitionis anticipated. For example, if the third control strategy is chosenbased on minimizing the cost function, the heater will transition orremain on and the wake up time will be determined to be approximately 30minutes for time step durations of 10 minutes (i.e., 3 times steps*10minute duration for each time step). The system will reassess itssituation when it wakes up after the anticipated duration to determineif additional heating is required. If the system wakes up for any reasonbefore the anticipated duration, the system will likewise reassess itssituation.

In some embodiments, the system may automatically remain on whenever thesystem is outside the maintenance band. For example, as shown in FIG.17, for each control strategy, the heater input at time i is always 1meaning the heater will remain on as long as the ambient temperature isbelow the lower maintenance band threshold. When the ambient temperaturecrosses the lower maintenance band threshold, however, the selectedcontrol strategies may change, which allows for the heater input tocycle off. The above defined subset of control strategies may again beconsidered when the ambient temperature drops below the lowermaintenance band threshold.

Similarly, the system may automatically cycle off when the ambienttemperature crosses the upper maintenance band threshold even if theanticipated wake up time has not occurred. For example, if the predictedtemperature rise was inaccurate and the temperature rise was greaterthan anticipated so that the ambient temperature rises above the uppermaintenance band threshold, the system will wake up and turn the heateroff to prevent further heating of the enclosure. In this manner, theconventional bang-bang control may function as a backup control toensure that too little or too much heating does not occur.

FIG. 18 illustrates a defined subset of control strategies that may beconsidered when the ambient temperature is above the upper maintenanceband threshold. As shown in FIG. 18, a first control strategy considersheater off-time cycles at six consecutive time steps (i.e., time step ito i+5) for an overall on-time percentage of 0. In contrast, the sixthcontrol strategy considers a single heater off-time cycle at a firsttime step (i.e., i) followed by five consecutive heater on-time cyclesfor an overall on-time percentage of approximately ⅚. The interveningcontrol strategies (i.e., 2-5) each consider an additional heateron-time cycle occurring for an additional time step relative to theprevious control strategy. As described previously, the defined controlstrategy may be further limited when a cycle on/off limit so dictatessuch as to avoid cycling the heater on/off after a previous cycletransition.

The cost function may be minimized to determine the most appropriatecontrol strategy and the current heater state (i.e., on or off) and wakeup time may be determined as described above. The system will reassessits situation when it wakes up to determine if the heater shouldtransition on or remain on. In some embodiments, the system mayautomatically remain off whenever the system is outside the maintenanceband. For example, as shown in FIG. 18, the heater input at time i isalways 0 meaning the heater will remain off as long as the ambienttemperature is above the upper maintenance band threshold. When theambient temperature crosses the upper maintenance band threshold,however, the control strategies may change to allow the heater input tocycle on. The above defined subset of control strategies may again beconsidered when the ambient temperature rises above the uppermaintenance band threshold. Similarly, the system may automaticallycycle on when the lower maintenance band threshold is crossed even ifthe anticipated wake up time has not occurred. As described above, thismay be useful when the predicted temperature drop is inaccurate and thetemperature drop is greater than anticipated.

Since in some embodiments, the heater function will not cycle off untilthe lower maintenance band threshold is crossed and similarly will notcycle on until the upper maintenance band threshold is crossed, itshould be appreciated that in these embodiments it may be desirable towiden the maintenance band (i.e., increase the maintenance band offsetvalue) to allow the heater to more quickly cycle on and off.Accordingly, in some embodiments, the maintenance band threshold valuemay be increased and the maintenance band widened based on how well theabove described prediction model represents the variance in temperaturefor the enclosure. For example, the maintenance band threshold may bebased on the calculated goodness of fit for the predictive model. Whenthe goodness of fit is relatively high showing that the predictive modelrepresents the temperature variance well, the maintenance band may bewidened and the maintenance band threshold increased. Likewise, when thegoodness of fit is relatively low showing less of a correlation betweenthe prediction model and the temperature variance, the maintenance bandmay be narrowed and the maintenance band threshold decreased.

In one embodiment, when the goodness of fit is above 0.7, themaintenance band threshold may be set at ±1.5 degrees Fahrenheit fromthe setpoint temperature. When the goodness of fit is between 0.5 and0.7, the maintenance band threshold may be set at ±1.0 degreesFahrenheit from the setpoint temperature. When the goodness of fit isbetween 0.3 and 0.5, the maintenance band threshold may be set at ±0.7degrees Fahrenheit from the setpoint temperature, which represents athreshold typically used by conventional bang-bang controls. If thegoodness of fit is below 0.3, the system may determine that thepredictive controls should not be used and conventional bang-bangcontrols may be used.

FIG. 19 illustrates a defined subset of control strategies that may beconsidered when the ambient temperature is within the maintenance band.The control strategies shown in FIG. 19 may essentially be a combinationof the control strategies described in FIGS. 17 & 18. The controlstrategies of FIG. 19, however, allow the heater to either cycle on oroff without crossing a temperature threshold. For example, if theambient temperature is within the maintenance band, the thermostat maydetermine a strategy that involves some amount of heating (i.e.,strategies 2-5 & 8-12), an entire heating cycle (i.e., strategy 7), orno amount of heating (i.e., strategy 1) and is not limited to an eitheron-time cycle or off-time cycle until a temperature threshold iscrossed. Further, the thermostat may determine to apply heating early inthe control duration (i.e., strategies 7-12) or later in the controlduration (i.e., strategies 1-6). As described previously, the definedcontrol strategy may be further limited when a cycle on/off limit sodictates such as to avoid turning the heater on after a previous offtransition.

The cost function may be minimized to determine the most appropriatecontrol strategy and the current heater state (i.e., on or off) and wakeup time may be determined as described above. The system will reassessits situation when it wakes up to determine if the heater shouldtransition on or off, or remain in a current state.

In some embodiments, the system may also determine which control optionis most appropriate for heating a home. For example, the system mayevaluate whether a bang-bang control, as described herein, or apredictive control is more likely to provide a desired result.

The above described, some portion of the above described operations, orsubstantially all the operations, may be performed on either the headunit processor or the backplate processor. In some embodiments, thepredictive temperature calculations, cost function minimization, and thelike may be performed by the head unit processor since this processor istypically more powerful. The determined wake up time and/or on/offcondition of the HVAC system may then be passed to the backplateprocessor for monitoring and wake up purposes. The backplate may wake upthe head unit at the determined wake up time or for any other reason andthe head unit may reevaluate its state and perform a state transition ifnecessary, or calculate and select another control strategy.

In some embodiments, the calculation described herein may be determinedeach time the head unit wakes up. The head unit may be awoken forvarious reasons, such as to look at a future setpoint, in response to atemperature adjustment by a user, based on crossing a lower or uppermaintenance band, based on a defined change of state (e.g., turning aheating operation on or off based on the predictive control), and thelike. For example, based on a selected predictive control with a lowestcost value, it may be determined to wake the head unit up after twentyminutes of heating. Upon waking the head unit up after this twentyminute time interval, the head unit may perform the calculationsdescribed herein and minimize a cost value to determine if additionalheating is appropriate or if the heating operation should be terminated.The head unit may likewise be woken up and a calculation performed todetermine if heating should begin or if the HVAC unit may remain off. Ifthe head unit is awoken for any other reason (e.g., a proximity sensoris tripped, and the like), the above described calculations may beperformed to adjust the heating control strategy and/or minimize costfunctions for various heating strategies.

In some embodiments, the radiant features described herein may beimplemented as default systems in the thermostat. For example, a usermay identify a home's heating as radiant, in which the predictivecontrol algorithms described herein would be automatically applied. Insome embodiments, the user may opt out of using the radiant heatingalgorithms by selecting an appropriate on/off button or feature thatdisables the predictive control algorithm. According to anotherembodiment, the user may be prompted to enable or disable the radiantheating features described herein, or may otherwise be required toenable these features before they are applied.

Exemplary Methods

Referring now to FIG. 20, illustrated is a method 2000 of controlling athermostat using a model predictive control. At block 20002, athermostat is provided. As described herein, the thermostat may includea housing, a memory, and a processing system disposed within thehousing. The processing system may be in operative communication withone or more temperature sensors to determine an ambient temperature inan enclosure and may be in operative communication with the memory. Theprocessing system may also be in operative communication with a radiantheating system to heat the enclosure via radiant heating so that theambient temperature is near a setpoint temperature.

At block 2004, a parameterized model is determined from which apredicted value for the ambient temperature of the enclosure responsiveto a candidate radiant heating control strategy may be determined. Theparameterized model may be based on historical ambient temperatures forthe enclosure acquired by the thermostat during associated historicalperiods in which radiant heat control was actuated by the thermostat andstored in the memory. At block 2006, a set of candidate controlstrategies may be selected for use in controlling the radiant heatingsystem. Each candidate control strategy may be a binary-valued controltrajectory having a candidate overall on-time percentage over apredefined candidate control duration. Further, each candidate controlstrategy may be constrained to have a minimum number of on-time cyclesthat achieves the candidate overall on-time percentage.

At block 2008, a predictive algorithm may be executed to determine anoptimal control strategy from the set of candidate control strategies.According to one embodiment, executing the predictive algorithm mayinclude: applying each candidate control strategy to the parameterizedmodel to predict a corresponding ambient temperature trajectory, andprocessing each corresponding ambient temperature trajectory in view ofone or more predetermined assessment functions to select an optimalcandidate control strategy according to one or more predeterminedassessment criteria. At block 2010, the radiant heating system may becontrolled according to the selected optimal control strategy.

The method may also include determining whether the model predictivecontrol provides enhanced control of the radiant heating system relativeto an additional control method prior to using the model predictivecontrol. If enhanced control is not provided, the thermostat maydetermine to use conventional control methods, such as bang-bangcontrols. The method may further include determining a Lag value thatrepresents an amount of thermal mass or inertia for the enclosure. Inone embodiment, the one or more predetermined assessment functions mayinclude a cost function, in which a cost is increased as an ambienttemperature trajectory of a respective candidate control strategydeviates from the setpoint temperature.

In some embodiments, the on-time cycles and off-time cycles may haveintervals of not less than 10 minutes. Such intervals may minimize noiseand/or reduce cycle on/off transitions and/or head unit wake upoccurrences. The parameterized model may include predetermined responsetrajectories, and the method may additionally include determiningweighting coefficients for the predetermined response trajectories. Inone embodiment, the parameterized model may be based on a combination ofhistorical solar radiation and a radiant heating response data acquiredduring associated historical periods. In such embodiments, applying eachcandidate control strategy to the parameterized model may include usinga solar radiation function and a radiant heating response function topredict the corresponding ambient temperature trajectory.

In another embodiment, the parameterized model may be based onhistorical outside temperature data acquired during associatedhistorical periods and applying each candidate control strategy to theparameterized model may include using forecasted temperature data topredict the corresponding ambient temperature trajectory. In someembodiments, the method may additionally include limiting a cycletransition of the radiant heating system while the ambient temperatureis outside a defined maintenance band threshold associated with thesetpoint temperature. The method may further include adjusting an offsetvalue of a maintenance band that defines an upper threshold temperatureand a lower threshold temperature relative to the setpoint temperaturebased on a confidence that the parameterized model characterizes thehistorical ambient temperature data.

Referring now to FIG. 21, illustrated is a method 2100 of controlling athermostat. At block 2102, a thermostat may be provided. The thermostatmay include a housing, a memory, and a processing system disposed withinthe housing as described herein. At block 2104, a parameterized modelmay be determined from which a predicted value for the ambienttemperature of the enclosure responsive to a candidate radiant heatingcontrol strategy may be determined. The parameterized model may be basedon historical ambient temperatures for the enclosure acquired by thethermostat during associated historical periods in which radiant heatcontrol was actuated by the thermostat and stored in the memory. Theparameterized model may also have a first confidence metric associatedwith it.

At block 2106, a maintenance band may be determined for operation of theradiant heating system. The maintenance band may have an offset valuethat defines an upper threshold temperature and a lower thresholdtemperature relative to a setpoint temperature. The upper thresholdtemperature and a lower threshold temperature may be used in controllingon-cycle and off-cycle transitions of the radiant heating system. Atblock 2108, the offset value may be adjusted based on the firstconfidence metric such that the offset value is greater if the firstconfidence metric is large, and the offset value is smaller if the firstconfidence metric is small.

At block 2110, a predictive algorithm may be executed to determine anoptimal control strategy from a set of candidate control strategies.According to one embodiment, the predictive algorithm may be executed byapplying each candidate control strategy to the parameterized model topredict a corresponding ambient temperature trajectory. AT block 2112,the radiant heating system may be controlled according to the determinedoptimal control strategy using the maintenance band.

FIG. 22 illustrates steps for automated system matching that arepreferably carried out by the same thermostat or thermostatic controlsystem that carries out one or more of the other HVAC control methodsthat are described in the instant patent specification. It has beenfound particularly desirable to make thermostat setup and governance asuser-friendly as possible by judiciously automating the selection ofwhich among a variety of available energy-saving and comfort-promotingcontrol algorithms are appropriate for the particular HVAC configurationof the home in which the thermostat is installed. At step 2202, the HVACsystem features available for control by the thermostat are determinedby virtue of at least one of (i) automated wire insertion detection,(ii) interactive user interview, (iii) automated inferences ordeductions based on automated trial runs of the HVAC system at or nearthe time of thermostat installation, and (iv) automated inferences ordeductions based on observed system behaviors or performance. Examplesof such methods are described in one or more of the commonly assignedUS20120130679A1 and US20120203379A1, as well as U.S. Ser. No.13/632,148, supra.

In relation to cooling mode operation, if it is determined that the HVACsystem includes air conditioning (step 2204), which may be by virtue ofa dedicated air conditioning system and/or a heat pump operating in thecooling direction, then at step 2206 there is enabled a smartpreconditioning feature for cooling mode operation. One example of aparticularly advantageous smart preconditioning feature is described inU.S. Ser. No. 13/632,150, supra. For some embodiments, the smartpreconditioning algorithm is configured to: constantly learn how fastthe home heats up or cools down by monitoring the recent heating andcooling history of the home, optionally incorporating externalenvironmental information such as outside temperatures, sun heatingeffects, etc.; predict how long the HVAC system will need to activelyheat or cool in order to reach a particular scheduled setpoint; andbegin preconditioning toward the particular scheduled setpoint at justthe right time such that the scheduled setpoint temperature will bereached at the scheduled setpoint time. User comfort is promoted byvirtue of not reaching the scheduled setpoint temperature too late,while energy savings is promoted by virtue of not reaching the scheduledsetpoint temperature too early.

In relation to heating mode operation, if it is determined that the HVACsystem includes radiant heating (step 2208), then at step 2218 there isenabled a smart radiant control feature for heating mode operation. Oneexample of a particularly advantageous smart radiant control feature isdescribed herein. For some embodiments, the smart radiant controlfeature is configured to monitor radiant heating cycles on an ongoingbasis, compute an estimated thermal model of the home as heated by theradiant system, and predictively control the radiant system in a mannerthat takes into account the thermal model of the house, the time of day,and the previous heat cycle information. The smart radiant controlfeature is configured to achieve comfortable maintenance bandtemperatures while also minimizing frequent changes in HVAC on/offstates and minimizing HVAC energy consumption. Among other advantages,uncomfortable and energy-wasting target temperature overshoots areavoided.

If it is determined that the HVAC system includes a heat pump includingauxiliary resistive electrical heating (i.e., so-called auxiliary or AUXheat) (step 2210), and if it is further determined (step 2212) that thethermostat is network-connected (such that it can receive outsidetemperature information based on location data and an internet-basedtemperature information source) or otherwise has access to outsidetemperature information (such as by wired or wireless connection to anoutside temperature sensor), then at step 2216 a smart heat pump controlfeature is enabled. If at step 2210 there is not a heat pump with AUXheat (which will most commonly be because there is a conventional gasfurnace instead of a heat pump, or else because there is a heat pump ina so-called dual-fuel system that does not include AUX heat), then atstep 2214 there is enabled a smart preconditioning feature for heatmode, which can be a similar or identical opposing counterpart to thepreconditioning feature for cooling mode discussed supra with respect tostep 2206. Similarly, if at step 2212 there is no network connectivityor other access to outside temperature information, then the smart heatpump control feature of step 2216 is not enabled and instead the smartpreconditioning feature of step 2214 is enabled.

In reference to step 2216, one example of a particularly advantageoussmart heat pump control feature is described in U.S. Ser. No.13/632,093, supra. Although the AUX heat function allows for fasterheating of the home, which can be particularly useful at lower outsidetemperatures at which heat pump compressors alone are of lesserefficacy, the energy costs of using AUX heat can often be two to fivetimes as high as the energy costs of using the heat pump alone. For someembodiments, the smart heat pump control feature is configured tomonitor heat pump heating cycles on an ongoing basis, tracking how fastthe home is heated (for example, in units of degrees F. per hour) by theheat pump compressor alone in view of the associated outside airtemperatures. Based on computed correlations between effective heatingrates and outside air temperatures, and further including a userpreference setting in a range from “Max Comfort” to “Max Savings”(including a “Balanced” selection in between these end points), thesmart heat pump control feature judiciously activates the AUX heatingfunction in a manner that achieves an appropriate balance between usercomfort and AUX heating costs. For some embodiments, the factorsaffecting the judicious invocation of AUX heat include (i) a predictedamount of time needed for the heat pump alone to achieve the currenttemperature setpoint, (ii) whether the current temperature setpointresulted from an immediate user control input versus whether it was ascheduled temperature setpoint, and (iii) the particular selected userpreference within the “Max Comfort” to “Max Savings” range. Generallyspeaking, the AUX function determination will be more favorable toinvoking AUX heat as the compressor-alone time estimate increases, morefavorable to invoking AUX heat for immediate user control inputs versusscheduled setpoints, and more favorable to invoking AUX heat for “MaxComfort” directed preferences than for “Max Savings” directedpreferences.

For some embodiments, the smart heat pump control feature furtherprovides for automated adjustment of a so-called AUX lockouttemperature, which corresponds to an outside air temperature above whichthe AUX heat will never be turned on, based on the monitored heat pumpheating cycle information and the user preference between “Max Comfort”and “Max Savings.” Generally speaking, the AUX lockout temperatures willbe lower (leading to less AUX usage) for better-performing heat pumps,and will also be lower (leading to less AUX usage) as the userpreference tends toward “Max Savings”. For some embodiments in whichthere is network connectivity available such that overnight temperatureforecasts can be provided, the smart heat pump control feature furtherprovides for night time temperature economization in which an overnightsetpoint temperature may be raised higher than a normally scheduledovernight setpoint if, based on the overnight temperature forecast, theAUX function would be required to reach a morning setpoint temperaturefrom the normal overnight setpoint temperature when morning comes.Advantageously, in such situations, even though the overnighttemperature inside the home is made higher it would otherwise be, theuser actually saves energy and money by avoiding the use of the AUXfunction when morning comes.

According to some embodiments, the determinations made at one or more ofsteps 2208 and 2210 can be based on automatically observed HVAC systemperformance information rather than specific system identificationinformation. For example, it may be the case that a particular heatingfunctionality of an HVAC system is not physically a radiant system, butnevertheless tends to exhibit signs of a high thermal mass combined withsubstantial control lag, making it similar in nature to a radiantheating system. For such cases, the smart radiant control feature may beenabled to improve performance. Likewise, it may not be the case thatthe HVAC system has a heat pump with AUX functionality, but it may havea two-stage heating functionality in which the first stage (which typewas likely chosen as a first stage because it was more cost-effective)tends to be very slow or “fall behind” at lower outside temperatures,and in which the second stage (which type was likely chosen as a secondstage because it was less cost-effective) tends to be verytime-effective in heating up the home, thus making the system act verymuch like a heat pump system with AUX functionality. For such cases, thesmart heat pump control feature may be enabled to improve performance.

Although embodiments of the invention have been generally directedtoward controls for HVAC systems, it should be realized that theconcepts described herein can be employed to control various othersystems or devices. For example, the idea of using historical data togenerate predictive controls may be used to control various homeappliances or systems. For example, homes fitted with proximity sensorsmay be used to detect the activity or occupancy level within the home(i.e., how active the home's occupants are throughout the day and inwhat locations they are active). This activity level data may berecorded and used to generate a predictive model of the home's activitylevels. Based on this model the home's appliances may be controlled. Forexample, the lights in historically less active areas may be dimmed,such as in late evenings when occupants are less active, or the lightsmay be gradually turned on as the occupants arise in the morning.

This concept may also be applied to control external devices or systems,such as a sprinkler system. For example, rain fall of the surroundingarea may be measured and recorded or otherwise obtained, and this datamay be compared to charts or graphs showing how much water a typicallyor average lawn in the area needs. A predictive model may be fit withthis data to adjust a sprinkler's watering times and/or volume based onpredictive or forecasted rain fall. This data may be also be provided tocity or state services to help these services predict or plan for theneeds of future homes within the area.

Whereas many alterations and modifications of the present invention willno doubt become apparent to a person of ordinary skill in the art afterhaving read the foregoing description, it is to be understood that theparticular embodiments shown and described by way of illustration are inno way intended to be considered limiting. Therefore, reference to thedetails of the preferred embodiments is not intended to limit theirscope.

What is claimed is:
 1. A thermostat comprising: a housing; a memory; anda processing system disposed within the housing and being in operativecommunication with the memory and with a radiant heating system, theprocessing system being configured and programmed to control the radiantheating system according to the steps of: determining a parameterizedmodel from which a temperature response of an enclosure responsive to acandidate radiant heating control strategy may be determined, theparameterized model being based at least in part on historicaltemperature information stored in said memory and acquired during atleast one historical period in which the enclosure was heated by theradiant heating system under the control of said thermostat; determiningan optimal radiant heating control strategy from among a plurality ofcandidate radiant heating control strategies by using said parameterizedmodel to compute a plurality of predicted temperature responsescorresponding respectively to the plurality of candidate radiant heatingcontrol strategies and processing said predicted temperature responsesaccording to one or more predetermined assessment criteria; andcontrolling the radiant heating system according to the determinedoptimal radiant heating control strategy.
 2. The thermostat of claim 1,wherein the memory is disposed within the housing, and wherein one ormore temperature sensors are also disposed within the housing.
 3. Thethermostat of claim 2, wherein the processing system includes a firstrelatively high-powered processor that is configured and programmed todetermine the parameterized model and to determine the optimal radiantheating control strategy by computing the plurality of predictedtemperature responses, and wherein the processing system includes asecond relatively low-powered processor that is in operativecommunication with the one or more temperature sensors to determine anambient temperature.
 4. The thermostat of claim 3, wherein thethermostat includes a rechargeable battery and the first processor isconfigured to transition between a wake state and a sleep state, whereineach time the first processor transitions from the sleep state to thewake state, the parameterized model and/or optimal radiant heat controlstrategy is re-determined based at least in part on ambient temperaturereadings determined by the second processor during the sleep state ofthe first processor.
 5. The thermostat of claim 1, wherein eachcandidate radiant heating control strategy is binary-valued.
 6. Thethermostat of claim 1, wherein a first optimal radiant heat strategycovering a first time period is determined and executed to control theradiant heating system during the first time period, and wherein asecond optimal radiant heat control strategy is determined during thefirst time period and executed prior to an end of the first time periodto control the radiant heating system.
 7. The thermostat of claim 1,wherein the processing system is further configured and programmed todetermine a Lag value that represents at least in part an amount ofsystem inertia for the enclosure.
 8. A method of controlling athermostat comprising: providing a thermostat comprising: a housing; amemory; and a processing system disposed within the housing and being inoperative communication with the memory and with a radiant heatingsystem, the processing system being configured and programmed to controlthe radiant heating system; determining a parameterized model from whicha temperature response of an enclosure responsive to a candidate radiantheating control strategy may be determined, the parameterized modelbeing based at least in part on historical temperature informationstored in said memory and acquired during at least one historical periodin which the enclosure was heated by the radiant heating system underthe control of said thermostat; determining an optimal radiant heatingcontrol strategy from among a plurality of candidate radiant heatingcontrol strategies by using said parameterized model to compute aplurality of predicted temperature responses corresponding respectivelyto the plurality of candidate radiant heating control strategies andprocessing said predicted temperature responses according to one or morepredetermined assessment criteria; and controlling the radiant heatingsystem according to the determined optimal radiant heating controlstrategy.
 9. The method of claim 8, wherein the memory is disposedwithin the housing, and wherein one or more temperature sensors are alsodisposed within the housing.
 10. The method of claim 9, wherein theprocessing system includes a first relatively high-powered processorthat is configured and programmed to determine the parameterized modeland to determine the optimal radiant heating control strategy bycomputing the plurality of predicted temperature responses, and whereinthe processing system includes a second relatively low-powered processorthat is in operative communication with the one or more temperaturesensors to determine an ambient temperature.
 11. The method of claim 10,wherein the thermostat includes a rechargeable battery and the firstprocessor is configured to transition between a wake state and a sleepstate, and wherein the method further comprises re-determining theparameterized model and/or optimal radiant heat control strategy eachtime the first processor transitions from the sleep state to the wakestate, the parameterized model and/or optimal radiant heat controlstrategy being re-determined based at least in part on ambienttemperature readings determined by the second processor during the sleepstate of the first processor.
 12. The method of claim 8, wherein eachcandidate radiant heating control strategy is binary-valued.
 13. Themethod of claim 8, further comprising: determining a first optimalradiant heat strategy covering a first time period; executing the firstoptimal radiant heat strategy to control the radiant heating systemduring the first time period; determining a second optimal radiant heatcontrol strategy during the first time period; and executing the secondoptimal radiant heat strategy prior to an end of the first time periodto control the radiant heating system.
 14. The method of claim 8,further comprising determining a Lag value that represents at least inpart an amount of system inertia for the enclosure.
 15. Acomputer-program product, tangibly embodied in a non-transitory machinereadable storage medium, including instructions configured to cause adata processing apparatus of a thermostat to: determine a parameterizedmodel from which a temperature response of an enclosure responsive to acandidate radiant heating control strategy may be determined, theparameterized model being based at least in part on historicaltemperature information stored in memory and acquired during at leastone historical period in which the enclosure was heated by a radiantheating system under the control of the thermostat; determine an optimalradiant heating control strategy from among a plurality of candidateradiant heating control strategies by using said parameterized model tocompute a plurality of predicted temperature responses correspondingrespectively to the plurality of candidate radiant heating controlstrategies and processing said predicted temperature responses accordingto one or more predetermined assessment criteria; and control theradiant heating system according to the determined optimal radiantheating control strategy.
 16. The computer-program product of claim 15,wherein the memory is disposed within a housing of the thermostat, andwherein one or more temperature sensors are also disposed within thehousing.
 17. The computer-program product of claim 16, wherein the dataprocessing apparatus of the thermostat includes a first relativelyhigh-powered processor that is configured and programmed to determinethe parameterized model and to determine the optimal radiant heatingcontrol strategy by computing the plurality of predicted temperatureresponses, and wherein the data processing apparatus includes a secondrelatively low-powered processor that is in operative communication withthe one or more temperature sensors to determine an ambient temperatureof the enclosure.
 18. The computer-program product of claim 17, whereinthe thermostat includes a rechargeable battery and the first processoris configured to transition between a wake state and a sleep state, andwherein the instructions are further configured to cause the dataprocessing apparatus to re-determine the parameterized model and/oroptimal radiant heat control strategy each time the first processortransitions from the sleep state to the wake state, the parameterizedmodel and/or optimal radiant heat control strategy being re-determinedbased at least in part on ambient temperature readings determined by thesecond processor during the sleep state of the first processor.
 19. Thecomputer-program product of claim 15, wherein each candidate radiantheating control strategy is binary-valued.
 20. The computer-programproduct of claim 15, wherein the instructions are further configured tocause the data processing apparatus to: determine a first optimalradiant heat strategy covering a first time period; execute the firstoptimal radiant heat strategy to control the radiant heating systemduring the first time period; determine a second optimal radiant heatcontrol strategy during the first time period; and execute the secondoptimal radiant heat strategy prior to an end of the first time periodto control the radiant heating system.